APEC 3611w: Environmental and Natural Resource Economics
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  1. 5. Earth Systems
  2. 15. Climate Change
  • Home
  • Syllabus
  • Assignments
    • Assigment 01
    • Assigment 02
    • Assigment 03
    • Weekly Questions 01
    • Weekly Questions 02
    • Weekly Questions 03
    • Weekly Questions 04
    • Weekly Questions 05
    • Weekly Questions 06
  • Midterm Exam
  • Final Exam
  • 1. Global Context
    • 1. Introduction
    • 2. The Doughnut
  • 2. Micro Foundations
    • 3. The Microfilling
    • 4. Supply and Demand
    • 5. Surplus and Welfare in Equilibrium
    • 6. Optimal Pollution
  • 3. Market Failure
    • 7. Market Failure
    • 8. Externalities
    • 9. Commons
  • 4. Macro Goals
    • 10. The Whole Economy
    • 11. Sustainable Development
    • 12. GDP and Discounting
    • 13. Inclusive Wealth
    • 14. Fisheries
  • 5. Earth Systems
    • 15. Climate Change
    • 16. Social Cost of Carbon
    • 17. Future Scenarios and SSPs
    • 18. Land Use Change
    • 19. Ecosystem Services
    • 20. Ecosystem Services, Hands-On
  • Games and Apps
  • Appendices
    • Appendix 01
    • Appendix 02
    • Appendix 03
    • Appendix 04
    • Appendix 05
    • Appendix 06
    • Appendix 07
    • Appendix 08
    • Appendix 09
    • Appendix 10
    • Appendix 11
    • Appendix 12

On this page

  • Resources
  • Content
    • Climate Change Economics: From Science to Policy
      • Course Logistics and the Final Project
      • The Basic Science of Climate Change
      • The Evidence for Human-Caused Climate Change
      • Temperature Trends and Recent Extremes
      • Climate Futures and Representative Concentration Pathways
      • The Human Costs of Climate Change
      • Economic Solutions to Climate Change
      • The DICE Model: An Integrated Assessment of Climate and the Economy
  • Transcript
  • Appendix
    • Climate Change as an Earth–Economy Problem
    • Learning objectives
    • Why climate is different
    • Climate as a stock–flow system
    • The climate externality
    • Core policy instruments
      • Carbon pricing
      • Standards and regulations
      • Public investment
    • Why partial thinking fails
    • Climate inside Earth–economy models
    • The Doughnut perspective
    • Open resources you can remix for this chapter
    • Exercises
    • Chapter roadmap
  1. 5. Earth Systems
  2. 15. Climate Change

Climate Change

The ultimate externality

Resources

Slides 15 - Climate Change

Country List and Statistics

Content

Climate Change Economics: From Science to Policy

This lecture marks a significant pivot in the course, shifting from the foundational microeconomic and macroeconomic theory covered in the first half to a more topics-focused approach centered on climate change, ecosystem services, and natural capital. Up until this point, the course has been very focused on the basics of environment and natural resource economics, including supply and demand models, cost-benefit analysis, and discounting. Now the focus shifts to applying those tools to specific, real-world topics. This also means that the style of assessment will change, becoming less micro-mathematics focused and more oriented toward having students answer questions with informed stances. This is a natural progression, going from detailed tools to more advanced understanding.

Course Logistics and the Final Project

Weekly Questions and Country Selection

A new weekly question has been posted, due on Friday of the current week. This question asks students to choose which country they will focus on for their final project, which is part of the shift toward more applied work. The final project will also earn students their writing credit required for graduation.

Students will indicate which countries from a provided list they are most interested in learning about. The instructor will use these responses to manually assign countries, ensuring full coverage across the class. Students should list their favorite few countries and write the most compelling argument for why they want to study those countries, as the most persuasive arguments will receive priority in assignment.

Assignment and Micro-Quiz Schedule Changes

An assignment will be posted on Canvas, due Monday of the following week, serving as preparation for the third micro-quiz. The schedule has been adjusted from previous iterations. Previously, the assignment was due right before the micro-quiz, which prevented the instructor from providing an answer sheet in advance. Under the new schedule, the assignment is due on Monday, the answer sheet is posted on Tuesday so students can study what they did or did not get right, and then students have Wednesday and Thursday to prepare for the micro-quiz on Friday. The micro-quiz will still be the same format: a very short assessment, probably one question, drawn very similarly from the assignment. This assignment and micro-quiz will focus on fisheries and/or discounting and net present value.

Final Project Overview

The final project is a significant part of the grade. Students will choose one country from a provided list and create an Earth Economy country report. This report will pull together information specific to the chosen country, including coverage of climate change — such as how exposed the country is, what its risks and opportunities are — as well as ecosystem services and natural capital present in that country.

The report will also incorporate some of the microeconomic tools learned in the first half of the course. The question students will address is what the basic tools in environment and natural resource economics say about their country and the incentives or policies that it is considering.

The project will use cutting-edge tools, including spatial data analyzed using geographic information systems (GIS). The course will start from no assumed knowledge about GIS, and students will come out of the project with basic GIS skills, which may be a marketable job skill. The instructor plans to switch to a classroom with table-style seating, as the project will involve hands-on exercises on computers with the instructor walking around to assist.

The Country List

The list of available countries is biased toward Central and South America. This focus was chosen for several reasons, the most important being that this is one of the areas that will be most critical for preserving nature. There is not a lot of high-value nature remaining in Europe or the United States compared to these locations. The instructor considered focusing on Africa as an alternative but chose Central and South America because of better data coverage.

The countries on the list range in size from the smallest, Jamaica, to the largest, Brazil. Because the project will involve computing actual models of climate change and ecosystem services, students may want to consider choosing a country that is not super large, unless they are confident in their computer skills, since larger countries require more computation.

The Basic Science of Climate Change

The Energy Budget of the Earth

The underlying dynamic of climate change is illustrated by the Earth’s energy budget, which is fundamentally a simple physics question. Energy comes in from the sun, with approximately 174,000 terawatts arriving at the top of the atmosphere. Just like a 1,500-watt space heater or a 140-watt laptop, this is a basic flow of energy, and the energy budget illustrates what happens to that energy.

The budget describes equilibrium conditions. Some energy is reflected by the atmosphere. Of the 174,000 terawatts coming in from space, roughly 10,000 terawatts bounce off the atmosphere, and another 35,000 terawatts bounce off clouds. The remainder makes it into the atmosphere and reaches the surface.

Reflection Versus Radiation

An important distinction in understanding climate change is the difference between reflecting and radiating energy. Although this sounds like a small linguistic difference, it is very different physically. Reflected energy behaves like a mirror: it comes in as light and leaves almost exactly the same, without being changed or absorbed. The reflected portions are the easy part — the energy simply gets sent back into space.

However, the majority of the problem is that not all energy is reflected. Much of it is absorbed by different parts of the Earth and the Earth system, and in the process of being absorbed, it is transformed from the energy in photons of light into infrared heat. Once absorbed, some of that energy will be re-radiated out to space, but the absorbed energy leaves behind heat. Unlike the reflected energy, where basically all of it bounces back out, the energy that is radiated back out leaves a residual of heat behind.

Some of this energy is absorbed by the atmosphere, some by land and oceans. For everything that is absorbed, some fraction bounces back out as heat into space instead of being perfectly mirrored. But the fundamental problem is that some of the radiation that would have gone back out to space is reabsorbed by the atmospheric layer. This protective layer, which shields the Earth from various hazards, captures outgoing infrared radiation and sends it back down to Earth. Understanding all of these different effects could fill a lifetime of scientific work.

Carbon Dioxide as the Central Molecule

The central molecule in this story is carbon dioxide (CO₂). A CO₂ molecule consists of one carbon atom and two oxygen atoms. We know this molecule very well because we are constantly breathing it out from our lungs. Carbon dioxide is exceptionally good at absorbing outgoing infrared radiation and re-radiating it back toward the Earth’s surface, which is why increasing its concentration in the atmosphere leads to warming.

The Evidence for Human-Caused Climate Change

Setting Aside Climate Skepticism on the Science

The lecture quickly addresses the arguments about climate change and the basis of evidence for it being human-caused, so that the class can set aside the scientific debate and focus on economics. The class is focused on what science says, and there is very little science supporting the climate skeptic position on whether climate change is happening.

However, the class will engage meaningfully with climate economics. Although there is very little debate about the basic facts — such as whether carbon dioxide concentrations have increased in the last hundred years — there is a great deal of debate about what we should do about it. This is where economics becomes valuable. The class will explore the full spectrum from the very conservative answer of “not a big problem, don’t worry about it” to much more progressive answers of “we need to act quickly, swiftly, and immediately.”

The focus will not be on skepticism that says climate change is not happening, but rather on using economics tools to analyze the cost-benefit analysis of climate mitigation. Economists are in a perfect situation to apply cost-benefit analysis, and different approaches within that method will illuminate the very conservative and very progressive political policies on climate change. The class is being inclusive across the political spectrum on the economics, just not on the basic science.

The 400,000-Year Record of CO₂ Concentrations

Carbon dioxide concentrations are measured in parts per million (PPM). Looking at the time series of CO₂ concentrations going back 400,000 years, which corresponds to how deep scientists can drill into ice sheets to observe tiny bubbles of air that were trapped there, we see all sorts of natural variation including ice ages. The climate skeptic point that climate has changed in the past is undeniably true. However, the question is simply whether the concentrations we see now are putting us in territory where we feel comfortable. The historical record shows that CO₂ concentrations swing around over time, but the current levels are going into wholly new and unprecedented territory.

The Industrial Revolution and the Great Acceleration

Zooming in to the period from roughly 0 BCE to 2000 CE, the carbon dioxide record shows no real change, just a stable level, until a sharp rise beginning shortly before 1800. What happened before 1800 was the Industrial Revolution. Prior to that, all the energy humanity relied upon came from the sun directly, going into plants. People could grow crops, have animals eat plants, and derive energy from those processes, but these were all relatively tiny sources of energy.

The Industrial Revolution introduced a wholly new source of energy that had never been significantly used before. Instead of relying on things coming from the sun in the short term, like wood, humanity began digging into the earth and extracting stored solar energy — plants and other organic matter that had been transformed over geological time into coal. This suddenly exploded the amount of energy available to humankind.

The rise begins with the Industrial Revolution, and energy usage becomes increasingly intensive over time. Then, in the middle of the twentieth century, essentially right after World War II, things accelerate dramatically. This is the great acceleration, which the course discussed previously. All sorts of indicators — per capita consumption, miles driven by cars, electricity usage — go exponential. This great acceleration is what drives the drastically higher concentration of CO₂ in the atmosphere.

The Mauna Loa Observatory and Direct Measurement

The era from 1960 onwards represents the modern science era, during which we have detailed records of a very simple experiment: capturing some of the atmosphere at a specific location and counting how many molecules of CO₂ are present. The Mauna Loa Observatory in Hawaii has the longest time series, though this measurement is now done at many locations around the world.

The data show two key features. First, there is an annual oscillation, representing the fact that the Earth breathes. There is much more vegetation in the Northern Hemisphere than the Southern Hemisphere, so photosynthetic activity during the northern summer causes CO₂ levels to cycle seasonally. This is predictable and should not distract from the overall trend. Second, the overall trend shows concentrations reaching unprecedented territory at approximately 420 parts per million.

This measurement is extraordinarily simple to replicate. A third-grade student could do it. The experiment requires only the ability to read numbers off of instruments and approximately $35 to buy a carbon sensor. Since the COVID pandemic, carbon sensors have become much cheaper because people have been using them to assess their exposure risk to COVID, reasoning that if CO₂ concentrations go up in a room, there are probably many humans nearby exhaling CO₂ and potentially pathogens. All one has to do is take a measurement one year, wait for the annual cycle to complete, and measure again the next year. Every single year, concentrations will be higher.

Temperature Reconstruction and the Modern Record

With more CO₂ in the atmosphere, the problem is that CO₂ is very effective at bouncing radiation back down toward the Earth’s surface. Looking at temperature reconstructed back to 100,000 years ago reveals a similar pattern of natural variation followed by an unprecedented spike in modern temperatures.

This is where skepticism becomes harder to dismiss with simple experiments. Measuring temperature, especially backwards into the past, requires more scientific understanding, including accounting for factors like sunspots and other natural variability. Nevertheless, it remains quite straightforward that we are in unprecedented territory.

Attribution Through Climate Models

Climate models are critical for understanding the human contribution to warming. The concept of models is familiar from the course, having encountered the circular flow diagram, the supply and demand model, and many other models. Models are toy representations of reality that allow us to ask “what if?” questions about what would have happened if something had been different.

Climate models allow for reconstruction into the past. Scientists simulate what happened, try to figure out what temperatures were, and match those reconstructions against what we do know from direct evidence, such as the concentrations of chemicals obtained from ice cores.

Once a model that understands the past is in hand, it can be used to ask a different type of “what if?” question about attribution. There is a model of temperature with humans — representing what was actually observed, since we are here producing emissions. Separately, a model of temperature without humans can be run, simulating only the natural forces that have changed over time.

This is important because the extent to which there is a gap between these two projections provides the answer to whether climate change is anthropogenic. When specified with this precision, we can even ask about the probability of being wrong. The 95th percentile confidence intervals are computed for both projections, and as soon as they diverge enough that the confidence intervals do not overlap, we can say with that degree of certainty that humans caused the change. And they have diverged — it is us.

A Long-Known Problem

The scientific understanding of this phenomenon has existed for a very long time. One of the earliest references to climate change appears in Popular Mechanics magazine from March 1912. The article discusses the tonnage of carbon dioxide being produced from burning 2 billion tons of coal per year and notes that the effects may be considerable in a few centuries. They had the basics right, and their prediction was correct.

Temperature Trends and Recent Extremes

Looking more closely at the recent record, global surface temperatures show the same rising pattern. These temperatures are expressed relative to the 1880 to 1920 mean, which is important because the baseline already includes some of the Industrial Revolution. The deviations would appear even larger if the comparison were to pre-Industrial Revolution temperatures, but direct observational records (literally from thermometers) do not extend back that far. The data could be reconstructed further back, but this record represents direct instrument-based observations.

The record shows variation, including a real flattening period that is often pointed to by climate skeptics, who note that there was concern about global cooling in the past. That is what that era represented. But that is old news. Since then, there has been an inexorable march upwards in temperatures.

Scientists have excellent explanations for what caused the flattening. The discussion does not dive into those explanations because the important point is that warming is happening and appears to possibly be happening at an exponentially increasing rate, especially in recent years. The weather in the last few years has been extreme, more extreme than scientists were expecting. Even the most pessimistic climate scientists have been surprised because temperatures have been worse than their projections.

One cannot draw a conclusion of great statistical certainty from only five or six years of above-trend temperature increases, but there comes a point where concern is warranted. At some point it becomes statistically relevant that the trajectory may not be a linear fit but perhaps something much scarier, like a tipping point.

Climate Futures and Representative Concentration Pathways

Projecting Forward with Scenarios

The question from an economics perspective does not depend much on where temperatures are now, but on where they are going. What will temperatures rise to if we continue on the current path?

Climate models are now used not to project backwards for validation, but to run forward. The course will increasingly use scenarios to explore the space of different possible futures. When projecting backwards, there is only one line — something specific happened, and while there may be uncertainty about exact values, there is no uncertainty about the specific series of events that occurred. But the future is different. It has not happened yet. Multiple options of what might happen must be considered, and these are called scenarios.

The RCP Framework

For climate, the scenarios are called Representative Concentration Pathways, abbreviated as RCPs. A Representative Concentration Pathway is essentially a set of assumptions about what humanity will do in the future with respect to emissions. The numbers associated with each RCP are the key indicators.

RCP 1.9 is the most positive frequently used scenario, representing little climate change. It represents a world where all Paris Agreement commitments are immediately implemented and warming is kept below 2 degrees Celsius. Running models under this scenario shows how beneficial such an outcome would be.

There are a range of scenarios between the best and worst possible outcomes. RCP 8.5 is the worst frequently used scenario, representing catastrophic climate change. RCP 8.5 was defined a considerable number of years ago, and there is an encouraging note: twenty years ago, this scenario was a very plausible possibility. However, humanity has taken some positive steps — many agreements have been signed, solar power has become much cheaper — and it is now becoming consensus that unless something unexpected happens, such as crossing a tipping point that causes methane to begin pumping out of permafrost, it is very unlikely that RCP 8.5 will be reached.

This is genuinely good news. There is even an argument that using RCP 8.5 represents a form of climate alarmism. The more appropriate interpretation is that humanity should be commended for having slowed down emissions growth. Nevertheless, the full spectrum of climate futures will be used to analyze the costs and benefits of different policy options, from aggressive mitigation now to essentially ignoring the problem.

The Human Costs of Climate Change

Uninhabitable Zones and Migration

Much of the analysis of climate change costs comes down to understanding the impacts. Under middle-of-the-road RCPs, large areas of the planet become simply uninhabitable due to extreme heat. This causes knock-on effects beyond direct mortality, including massive migration, which is a politically sensitive issue.

Wet Bulb Temperature and Physiological Limits

One of the most important and undeniable limits that climate change may impose comes from the question of how hot the human body can physically survive. Research by Matt Huber and colleagues, published in 2010, examined the physiological response of human bodies to extreme heat. Although global temperatures may not universally reach the most extreme projections, in some locations temperatures could rise above 7 degrees Celsius of warming.

The research introduces the concept of wet bulb temperature, which is measured by wrapping a thermometer in water to allow evaporation, mimicking the body’s cooling mechanism. Wet bulb temperature is the best indicator of whether a human can survive in given conditions.

Under super extreme conditions at certain temperatures, no amount of personal mitigation or self-protection besides actively pumping electricity into air conditioners would work. At those temperatures, even if a person were standing in gale force winds, completely doused with water, wearing no clothing to maximize heat dissipation, and critically not working at all — standing as still as possible to minimize heart rate — the body would still reach lethal temperatures. There is a definite, known point where the human body physically stops functioning.

A Fictional but Grounded Depiction

Kim Stanley Robinson’s novel The Ministry for the Future opens with a vivid depiction of what happens when a heat wave passes the wet bulb maximum temperature that the human body can tolerate. Set in India, the opening describes a cascading power failure where the entire electrical grid goes down, eliminating air conditioning, and resulting in a horrific catastrophe. The novel uses this catastrophe as a galvanizing force for societies to come together to try to solve climate change. While devastating in its opening, the book takes a somewhat positive trajectory from that point.

Economic Solutions to Climate Change

The Carbon Tax as Consensus Solution

There is essentially consensus across the political spectrum of economists that the best solution to climate change would be to implement a tax on carbon. Even conservative economists agree on this point. The basic mechanics are straightforward: put a tax in place that makes the marginal costs to society, including all climate damages, equal to the marginal costs faced by the producers of carbon-emitting technologies. This would make carbon-intensive energy sources like coal much more expensive.

The problem is that there does not seem to be sufficient political willpower to implement such a tax. In a certain sense, there is not much to debate — the optimal solution is known. However, underneath this basic answer lies enormous complexity about specifics.

The Discount Rate and the Value of the Future

One of the most critical factors in climate economics is the discount rate, which governs how much we care about the future versus the present. This concept was already explored earlier in the course in the context of fisheries and was shown to change optimal decisions dramatically for any resource problem involving time.

The impact of discounting is especially dramatic when applied to climate change. The basic idea is that a changing interest rate and a changing view of how far into the future one looks will drastically alter how much future values are worth today.

A stark example: if one stood to gain $1,000 of value, delivered as a check in 100 years, and the applicable discount rate is 10 percent (which is close to the market discount rate for investments), that future payment is worth only 7 cents today. One should pay no more than 7 cents for such a contract.

In the climate context, this means that almost any massively beneficial policy that might reduce climate damages in the future is going to be worth very little today. This simply reflects the fact that the benefits of climate action will be felt by future generations, not the current generation making the investment.

To put a concrete number on this, suppose climate change is going to cause $1 trillion in damages by the year 2100. If a discount rate of 3 percent is assumed, it would only be optimal, following cost-benefit analysis, to spend $7 billion to avoid those damages. This result is problematic from many perspectives, but it is the straightforward output of standard cost-benefit analysis when applied to long time horizons.

The DICE Model: An Integrated Assessment of Climate and the Economy

What are Integrated Assessment Models?

Integrated Assessment Models (IAMs) combine some representation of the environment with some representation of the economy. They are the primary tools economists use to analyze climate policy.

Introduction to the DICE Model

The Dynamic Integrated Model of Climate and the Economy (DICE model) is the most famous integrated assessment model and was created by William Nordhaus. The course examines this model for two reasons.

First, it is a genuinely impressive economics model. It won its inventor the Nobel Prize in Economics in 2018. It draws directly on material covered earlier in the course, namely the Ramsey model of economic growth.

Second, the DICE model is the basis for much climate skepticism from economists. The DICE model has been used to argue, using economic logic that is quite solid internally, that it is not optimal to spend very much money on abating climate change. This has led to skepticism of a particular type — not skepticism about whether climate change is happening, but skepticism that says climate change is happening yet it is not optimal to spend very much on dealing with it. The class will engage with this question very directly.

The Structure of the DICE Model

The DICE model is an integrated assessment model that links economic activity to climate change through cost-benefit analysis. It couples a climate model that takes emissions from economic activity with intertemporal optimization. The optimization asks: what is the optimal amount of emissions and emissions abatement over time in order to maximize the net present value of consumption?

This structure is not far from standard macroeconomics. It uses a growth model to determine optimal growth and optimal decisions that maximize welfare, but adds an extra dimension: the option to invest in climate mitigation, where the benefit of that investment is that future consumption might be higher because climate change damages are reduced.

In summary, the DICE model is a cost-benefit optimization that integrates climate and economy.

History of the DICE Model

The most recent version discussed in the course is the 2023 version of the DICE model. The first version dates to 1992, making it the first integrated climate-economy optimization model. It has been continuously updated over the years, reflecting additional years of observations, what countries have actually done in terms of emissions policy, and the latest climate science. It was this long history of model development and refinement that led to Nordhaus winning the Nobel Prize.

The Three Components of the DICE Model

The model works through a basic linkage. An economic model produces emissions, which affect two things. First, the emissions feed into a carbon cycle module that tracks how much carbon is stored in the atmosphere, the upper oceans, and the deep oceans, thereby tracking CO₂ concentrations. Second, the model calculates from those emissions the amount of climate change (temperature increase) that results. These two outputs are then combined into a damage function, which quantifies how much loss to economic activity occurs as a function of the temperature increase.

The Welfare Maximization Problem

The DICE model maximizes welfare, where welfare is defined as the sum over all time periods into the future of a utility function, representing the idea that humans derive happiness from consumption. This is identical in structure to what the course covered previously with the Ramsey model.

The welfare function is linked to population growth, because the model is applied to the real economy rather than a purely theoretical one, so it must keep track of how many people there are.

The welfare function also includes a discount factor, which is a simplification where the discount rate is raised to a negative time power. This is the mathematical mechanism by which utility in a distant time period (such as period 100) is reduced by a very large amount.

A final term allows the model to consider time steps, so the utility can be computed over intervals such as every five years.

The Damage Function

The real twist of the DICE model is that the welfare maximization is subject to a damage function. In the Nordhaus formulation, the damage function takes temperature (uppercase T) as its input — where lowercase t represents time and uppercase T represents temperature. The damage function is one of the simplest possible specifications: the temperature multiplied by some coefficient, plus the temperature squared multiplied by another coefficient. The values of these two parameters are chosen to match the damage function to studies that have attempted to understand climate damages.

Concretely, the damage function looks like this: at 1 degree Celsius of warming, there is a tiny reduction in GDP, well below 1 percent. At 6 degrees Celsius of warming, there is a 19 percent loss of GDP. The curvature in the function between these points reflects the squared temperature term.

The coefficients in the damage function are calibration parameters — values drawn from the academic literature that make the shape of the function match empirical studies. Nordhaus examines a collection of studies in the literature and determines what parameter values make his equation best fit those studies.

Solving for the Optimal Amount of Climate Change

Given the structure of maximizing welfare subject to damages, where the model gets to choose how much climate change occurs through the level of abatement investment, Nordhaus solves for the optimal amount of climate change. The result is expressed as the social cost of carbon, which represents the dollar value of the damage caused by emitting one additional ton of carbon dioxide.

The Social Cost of Carbon from DICE

When Nordhaus runs the DICE model, it produces an estimate that removing one ton of carbon dioxide from the atmosphere is worth $51 of value. The challenge is that this number is very low compared to what many scientists and other economists have argued is the optimal amount.

This matters because the social cost of carbon is used in cost-benefit analysis for evaluating climate mitigation policies. If it is possible to reduce emissions at a cost less than $51 per ton, the model says that should be done. But anything that would cost more than $51 per ton should not be done, because it would make everyone worse off according to the analysis. The real problem is that there is not a great deal that can be done to reduce carbon emissions at this low price point.

The course will return to this result and examine other contending models, but the DICE model result serves as the skeptical baseline. At $51 per ton of carbon, the punchline of the Nordhaus analysis is that humanity should not do very much to address climate change. The next class will explore the social cost of carbon in more detail and examine the critiques of this approach.

Transcript

All right, everybody, let’s get started. We have a number of people being remote for this class because of the awful road conditions. Did anybody else get stranded?

No? I actually thought it wasn’t that bad. They canceled school for the St. Paul Public Schools, which means I have to go home and do childcare for the second half of the day, right after this, so I’m a little bit salty about that. I don’t think it was that bad at all.

Today, first off, welcome back from spring break. I hope everybody had a really awesome, relaxing time. I went to London, as I said, and then my flight back was supposed to land about two hours after the storm started, and so I get a text in midair while I’m going from London to Chicago saying my Chicago to Minneapolis trip had been canceled, and that there weren’t any flights available for four days.

So I’m thinking, oh no, I’m going to have to teach class from the airport. But then what I did is I sprinted and ran and got on the one flight that was still able to land, and I made it here. So I sprinted so I could see all of you today. That was close. That would have been really bad.

What we’re going to talk about today is climate change. This is going to be a big pivot in what the course is focused on, and I actually want to make a comment on that as we talk through the agenda of what we’ll really look at today.

This aligns really nicely. We just finished the midterm, and by the way, sorry I wasn’t there. I got pulled into some negotiations that were in our research centers, and so hopefully you didn’t miss me.

We’re going to be shifting, now that we’ve finished that and had our spring break, to be more topics-focused. Up until this point, we’ve been very focused on microeconomic and macroeconomic theory and tools, sort of the basics of environment and natural resources. But now we’re going to be switching to talking about specific topics, namely climate change and ecosystem services, along with natural capital.

That means also a lot of the questions that we’ll get on things like assignments and quizzes are going to be a little bit less micro-mathematics focused and a little bit more having you answer questions with informed stances. This is a natural progression, going from detailed tools to more advanced understanding, and I’m looking forward to that.

So that’s the change coming up, and we’re going to get that started by talking about climate change. I’ll start with a very fast overview of the basics of climate change. Some of you have had climate economics classes before. This will be focused a little bit more on the applied tools that we’re going to use in this class.

And then we’ll actually start in on one of the most famous economists and models, William Nordhaus and the DICE model, respectively, which is essentially the basis of a lot of climate skepticism, and we’ll build from there towards a better understanding.

Any questions about the agenda or where we’re going?

Good.

In terms of logistics, we have a new weekly question. I post it on the website, and right after class I’ll put it on Canvas, so it probably hasn’t sent you the announcement yet, but we will have new weekly questions due on Friday of this week.

This will have you choose which country you will focus on for your final project. I’ve alluded to that in the past. This is part of shifting to more applied work. This will also earn you your writing credit that you get from this class for graduation.

You’ll be talking about which countries from a list you are most interested in learning more about. I will also be using this to determine which country you actually get assigned. You’ll be listing your favorite few, and whoever writes the most compelling argument for their countries, I will then manually assign them so that we have full coverage. So do your best.

Next, there’s an assignment that will be posted shortly on Canvas, due Monday of next week. That will be a preparation for the third micro-quiz.

I’m going to change things up just a little bit. Before, I had the assignment due right before the micro-quiz. I didn’t quite like that because I couldn’t give you an answer sheet to the assignment.

So now the assignment is due on Monday. I’ll post the answer sheet so you can study what you did or didn’t get right, and I’ll post it right after it’s due, so it’ll go live on Tuesday for the answers, and then you’ll have Wednesday and Thursday to prepare for Friday, which is where we’ll have the micro-quiz. It’ll still be the same idea, a very short, probably one-question thing, drawn very similarly from the assignment. Does that sound better, rather than trying to jam the assignment right before the quiz?

I’m seeing some nodding, so good. We’re iterating on this as we go.

This assignment and micro-quiz will focus on fisheries and/or discounting and net present value, and you’ll see the details in the assignment that I’ll post.

The last logistic element is the final project. This is a big part of your grade, so you might be interested in this. Just some very loose details now, but I’ll write up a full assignment sheet that will go live soon. You will be choosing one country from a list. I’ll put it on the next slide, and it’s also online already. You will be creating an Earth Economy country report.

This will pull together information specific to your country that will include coverage of climate change, like how exposed is your country, what are its risks and opportunities. It will also talk about ecosystem services and natural capital present in that country. Both of these things we’ll obviously talk about in the next couple of weeks.

The hardest part might be that it will incorporate some of the microeconomic tools that we learned in the first half of the course. We’ll talk more about how that might work, but the question is: what do our basic tools in environment and natural resources say about your country and the incentives or policies that it’s considering?

The really cool thing is we’re going to be using some cutting-edge tools, including spatial data using geographic information systems. I know a number of you expressed interest in that. We’ll start from no assumed knowledge, so don’t worry about that. I’m going to teach you, and you’re going to come out of this with the basic workings of GIS skills, which might be a marketable job skill.

I’m even going to try to switch classrooms. I noticed that there was one available with more table-style seating, because I’ll actually be walking around and we’ll be doing hands-on exercises with different things on your computers. So that’s the final project.

Here is the official list. You can click on the link there. This is maybe my worst formatting I’ve ever done. But this is a full set of indicators, including a full set of countries that will be included. One thing you might notice is there’s a heavy bias to this list. It is all countries in Central and South America. I’ve decided to focus on those for a number of reasons. I think the biggest is that this is one of the areas that’s going to be most important for preserving nature. We don’t have a lot of nature of high value left in Europe or the United States compared to these locations. I was debating between South America and Africa, but we have a little bit better data coverage here, and so a lot of these countries will be the focus.

One thing I’ve also noted is the size of the countries, ranging from the smallest, Jamaica, up to the largest, Brazil. We’re going to be computing actual models of climate change and ecosystem services on these, so one thing you might want to consider is choosing a country that’s not super large, unless you’re pretty confident in your computer skills. That’s just one factor to consider. This is linked on the website.

Here are the slides for today. Here’s that country list. There’s a bunch more statistics than I showed, but that’s what you’ll be looking at. And in terms of assignments, these are all updated.

Any questions before we dive into content for today? I know there’s a lot of things to get oriented on, because now we’re going back full speed after a very slow and relaxing spring break.

Okay, well, let’s power on.

Climate change. It’s this thing that we haven’t talked all that much about yet, but it’s the huge elephant in the room. It’s a massively important thing in environment and natural resource economics, and there’s been a ton of interesting work on it. I want to start with understanding the basic science so we can talk meaningfully about this.

The underlying dynamic is illustrated here. This might be basic, but at the end of the day, this is a very simple physics question. We have energy coming in from the sun. All of these numbers listed here, like this 174, are in thousands of terawatts.

Just like our 1500-watt space heater, or your 140-watt laptop, we’re getting some basic flow of energy in from the sun, and it’s obviously massive. 174,000 terawatts is coming in, and this is a budget that illustrates what happens to that energy.

This describes the equilibrium conditions. We see some of it is reflected by the atmosphere. 174 comes in from space, 10 of it bounces off the atmosphere, 35 of it bounces off of the clouds, and the remainder is what makes it into the atmosphere.

One side note: we’re going to be talking about the difference between reflecting versus radiating. This sounds like a small linguistic difference, but it’s very different physically. Reflected means it’s like a mirror. It comes in as light and it leaves almost exactly the same. It hasn’t been changed and nothing has been absorbed.

So those reflected portions are the easy thing, where the energy just gets sent back into space.

But the majority of the problem is that it’s not all about reflection. It’s about being absorbed by different parts of our Earth and Earth system, being transformed from light and the energy in those photons into infrared heat.

What this means is that the energy will be absorbed, and some of it will then be re-radiated out to space. But once it’s been absorbed, a lot of it is going to get captured. Unlike the reflected energy, where basically all of it that gets reflected just bounces back out, the energy that is going to be radiated back out leaves behind a bunch of heat.

That’s the basic problem. Some of this gets absorbed by our atmosphere, some of it gets absorbed by land and oceans, and for everything that’s absorbed, some fraction of it bounces back out as heat into space. Instead of just mirroring it, it’s converting it to heat and sending it back out.

But the basic problem is that some of the radiation that would have gone back out to space is going to be reabsorbed by the atmospheric layer that protects us from all sorts of things, which gobbles it back up and sends it back down to Earth. These are the basics, and you could spend a lifetime of work understanding all of these different effects.

Basically what it comes down to is this: this is our bad guy. This is a very dated depiction of what a CO2 molecule looks like. It’s dated because atoms don’t really look like this. They are probability clouds of quantum mechanical interference, so they’re really not spheres. But the idea is you have a carbon atom and two oxygen atoms.

We know this particular molecule very well because we’re breathing it out. It’s coming out of our lungs, so it’s a very easy-to-understand thing.

What I want to do now is quickly talk through the arguments about climate change, about the basis of evidence for it being human-caused, so that we can quickly set it aside. We’re not going to spend a whole lot of time on climate skepticism, and that’s just because this class is very much focused on what science says, and there isn’t a whole lot of science on the climate skeptic side.

We will, however, engage really meaningfully with climate economics. Although there is very little debate about the basic facts, like has carbon dioxide concentration increased in the last hundred years, there is a whole lot of debate about what should we do about it. That’s where I love economics. We’re going to see the very conservative answer of “not a big problem, don’t worry about it,” which we’ll talk about today, to much more progressive answers of “we need to act quickly, swiftly, and immediately.”

What I’m saying is I’m not going to focus much on the skepticism that says it’s not happening, but I will focus on looking at what our economics tools do to let us analyze, essentially, the cost-benefit analysis of climate mitigation.

We’re in a perfect situation as economists to apply cost-benefit analysis, and different approaches in that method will let us explore the very conservative and very progressive-leaning political policies on climate change. So I’m still being very inclusive, just not on the science part.

Setting aside that debate, it’s just true. We’re in unprecedented territory, and the easiest way to think about that is looking at the time series of carbon dioxide concentrations.

I’m talking about CO2 PPM, which stands for parts per million. That’s the axis here, CO2 concentrations, and the horizontal axis is thousands of years ago. We’re looking at 400,000 years ago, and that corresponds basically to how deep you can drill into ice sheets to try to observe little bubbles of air that were laid down and trapped there 400,000 years ago.

It’s a pretty long time span, and we see all sorts of things like ice ages. This is one of the key skeptic points: climate has changed in the past. Well, yes it has. That’s no doubt. The question is simply whether the concentrations that we see now are putting us in an area that we feel comfortable being in. As you can see, it does swing around in the past, but we are going into wholly new territory, and that’s challenging.

This last little spike here is hard to see the detail, so this inset zooms in onto the years BCE to 2000. We see the basics of no real change, just kind of staying around a stable level, until this sharp rise. What happens here? What happened a little bit before 1800? Were there any inventions of note?

The Industrial Revolution. And so basically, before that, all the energy we were relying on was coming from the sun directly, in terms of going into plants. We could grow things, we could even have animals eat the plants, and that would give us energy. Those are all really tiny sources of energy. The Industrial Revolution essentially says there’s another source of energy, a wholly new one that was never hardly used.

Instead of relying on things coming from the sun, or at least short-term from the sun like wood, let’s dig into the earth and grab essentially stored sun. Plants and other things that had turned into coal. Suddenly, we just exploded the amount of energy available to humankind.

It starts there. We immediately start to get more intensive in our usages, but then something in the middle of the previous century, essentially right after World War II, things just go crazy. We’ve seen this before: it’s the great acceleration that we talked about. All sorts of things like per capita consumption, the amount of miles driven by cars, or the amount of electricity used all just go exponential.

That’s what’s leading us to this drastically higher concentration of CO2 in the atmosphere.

I’m not actually even talking about temperature yet. I’m talking about a very simple-to-observe biophysical metric. If you ever get into a debate with somebody who says climate change isn’t happening, I would argue these two things together are essentially undeniable.

The era from 1960 onwards represents the modern science era, where we have really good, detailed records of a really simple experiment. Let’s capture some of the atmosphere at a specific location, in this case the Mauna Loa Observatory, which has the longest time series, but we now do this in lots of places, and just count it up: how many molecules of CO2 are there? That’s the simplest experiment. What do we see?

Number one, you see that oscillation. This is just representing the fact that the Earth breathes. There’s a lot more vegetation in the Northern Hemisphere than the Southern Hemisphere, and so the amount of photosynthetic activity when the northern part is in summer is going to cause it to be higher and then go lower in the winter. That’s pretty predictable. Obviously, you don’t want to look at any particular month, but look at the overall trend, and we see we’re in uncharted territories of 420 parts per million.

If somebody says the scientists are making this up, this is why I’m not going to accept much climate skepticism in this class on the science. I could replicate this with my third-grade daughter easily. You need third-grade science level. You need to be able to read numbers off of instruments.

You also need maybe $35 to buy a carbon sensor. Actually, since COVID, carbon sensors have come way down in price because people are wearing them around to look at what their risk to COVID is. The thinking being that if your carbon dioxide concentration goes up, it’s probably because there’s a lot of humans around you pumping out carbon dioxide from their mouths. They’re not interested in climate change, but it’s a pretty good metric of your exposure to human pathogens.

All you have to do is take a measurement one year, wait for the annual cycle of ups and downs to change, and take it again the next year. Every single year, you will see that concentrations are going up.

That’s undeniable. I have very little patience for debating that one. If my daughter could replicate this, we’re just going to set that aside. If you doubt me, go buy a sensor on Amazon. There’s thousands of other ones out there. You can measure it yourself.

Setting that aside, then. As more CO2 is in the atmosphere, the problem is that CO2 is very good at bouncing that radiation back down on our Earth. Here we’re going to look at the plot of temperature reconstructed all the way back to 100,000 years ago. We’ve seen a similar plot before, and then the unprecedented spike in temperatures.

This is where there is more skepticism that’s harder to set aside with third-grade science. Measuring temperature, especially backwards into the past, requires a lot more scientific understanding, like what was going on with sunspots and other factors. It’s still quite straightforward, though, that we are in unprecedented territory.

More importantly, we have models that can try to ascertain what would have happened without the interference of humans in terms of putting more greenhouse gases in place.

Throughout this discussion of climate change over the next few days, we’re going to be returning to models. We’ve had tons of models in this course. We’ve had the circular flow diagram, we’ve had the supply and demand model, we’ve had many models, so we’re quite comfortable. These are toy representations of reality that let us ask “what if?” What if something had been different? That is relevant to understanding what the temperature would have been if we know the CO2. Can we make a good guess? That’s what “reconstructed” means.

Now, models. We’re going to be talking about climate models. They let us do the reconstruction into the past to say, let’s simulate out what happened, and try to figure out what the temperature was, and match it up with what we do know, like the concentrations of chemicals that we get from ice cores.

But once we have a model that understands it, a representation, we can then ask another type of what-if question about attribution. That’s where we have a model of temperature with humans. This is the one that we actually observed, because we’re here, we’re humans, we’re producing our emissions.

Without humans, we can use our representation, our toy representation, and say let’s try only simulating the natural forces that have changed. Why is this important? Because it gives us a nuanced understanding of whether it is anthropogenic. Is it humans that have caused this?

The extent to which there’s a gap between these two projections is the answer of whether or not humans are causing climate change. When we have it specified with this precision, we can even ask all sorts of things, like what’s the probability that we’re wrong?

These are the 95th percentile confidence intervals, and as soon as they diverge enough where our 95th percentile confidence intervals don’t overlap, then we can say with that degree of certainty that it was us. And it is.

I like this one. We’ve known about this for a long time. Here’s one of the earliest references to climate change. This is in Popular Mechanics, circa March 1912.

You can read the caption, but basically it’s saying exactly what we just talked about, discussing the tonnage of carbon dioxide coming from the fact that we’re burning, as they say, 2 billion tons of coal a year. They had the basics for a long time. They say the effects may be considerable in a few centuries. Well, they were right.

Let’s zoom in a little bit more on where we’ve been in the last set of years. We see this same pattern of rising global surface temperatures. This is relative to the 1880 to 1920 mean. It’s always important to keep track of what you’re comparing it to, so this is already including some of the Industrial Revolution.

These numbers would be a greater deviation if we looked at it going pre-Industrial Revolution. We just don’t have those numbers with as direct observations. We could reconstruct it, but here this is literally thermometers, and so people have been recording that information.

We see variation. We actually see a real flattening here, and this is what a lot of people point to when they’re expressing climate skepticism, saying that we’ve been worried about global cooling in the past. Well, that’s what this era was.

But that’s old news. We have seen this inexorable march upwards since then. Scientists have great explanations for what caused that flattening. We won’t dive into them because it’s happening, and it seems to be happening, possibly at an exponentially increasing rate, especially in these last few crazy years.

We’ve had some crazy weather in the last few years, and it’s more extreme than scientists were expecting it to be. Even the most pessimistic climate scientists have been surprised, because these last few years have been worse than they were expecting.

You can’t make a conclusion of much statistical certainty with only five or six years of above-trend temperature increases, but you do start to worry at some point, and at some point it does become statistically relevant that it’s not a linear fit, this green line, but perhaps something much scarier, like a tipping point.

That’s where it is. The question of what’s optimal from an economics perspective doesn’t depend much on where it is, but where is it going? What will temperatures go up to if we continue on this path that we’re on?

We have very sophisticated ways of talking about this, and we’re going to use our climate models again, but now instead of projecting backwards to figure out if those models are accurate, we’re going to start running them forwards. We’re going to have scenarios, which are increasingly going to become a big part of this class, that will let us explore the space of different possible futures.

When you’re going backwards, there’s only one line. There might be uncertainty on where exactly it was, but something specific happened. There’s no uncertainty on the series of events that occurred.

But the future is different. It hasn’t happened yet. We need to consider different options of what might happen, and we’re going to call those scenarios. For climate, we’re going to call them a particular type of scenarios known as the Representative Concentration Pathways. I’ll just say from now on, RCPs.

A Representative Concentration Pathway is essentially a set of assumptions about what we’re going to do in the future with respect to our emissions. The numbers here are the key thing you want to look at.

For Representative Concentration Pathways, 1.9 is the most positive, frequently used scenario, representing little climate change. What does this represent? It represents a world where we immediately implement all of our Paris commitments and keep ourselves below 2 degrees of warming. That would be great, so let’s run our models to see how great that would be.

But we’re also going to have a whole range of models in between the best possible and the worst possible. The worst frequently used scenario is RCP 8.5, representing catastrophic climate change.

What’s interesting about RCP 8.5 is that this was defined a good number of years ago, and if you’re feeling overwhelmed by all this doom and gloom, let me say a positive note: 20 years ago, this was a very possible possibility. But we’ve actually done some things. We’ve signed a lot of agreements, solar power has become much cheaper, and so now it’s becoming consensus that unless something unexpected happens, like we cross a tipping point and methane starts pumping out of our permafrost, it’s very unlikely that we’ll reach this.

So that’s good. There’s actually an argument to be made that it’s sort of climate alarmism to use this one. What I would say is that’s more like, great, we should be patting ourselves on the back, we have slowed down our emissions.

But what we’re going to do is use this spectrum of different climate futures to analyze what would be the costs and benefits of doing different policy options, like aggressive mitigation now versus just not caring about it.

A lot of that will come down to questions of costs. Will this be bad? You can read tons about that. This is the one that I always think the most about, and that’s the really hot areas. We know a lot about how a human can survive in extremely hot temperatures, and under the RCPs that are in the middle of the road, we have large areas that are simply uninhabitable. This causes knock-on effects besides death, like massive migration. It turns out migration and immigration may be a little bit of a politically sensitive issue.

The best way to think about this is actually one of my friends, Matt Huber, who was looking at one of the more undeniable limits that climate change might put us into, and that’s the question of how hot can we actually survive?

I actually don’t think this is what’s going to happen globally, but in some locations it will be true that we’ll be going above 7 degrees centigrade. In this particular paper from 2010, they looked at the physiological response of human bodies. Not economies, but bodies. They talk about a concept called wet bulb temperature.

This is essentially taking a thermometer and wrapping it in water to let that water evaporate and try to wick away heat. The temperature of that is the best indicator of whether we could survive.

They talked about super extreme conditions where, at certain temperatures, no amount of mitigation or protecting oneself besides pumping electricity into air conditioners would work. They talk about how, at those temperatures, even if you were in gale force winds cooling you down, completely doused with water, wearing no clothing (which would maximize heat dissipation), and critically not working, just standing as still as you can to reduce your heart rate, your body would still be at lethal temperatures.

I like to think about extremes. There is a point where the body physically stops working, and we know what that is.

Has anybody read this book? If you really want to be depressed, I’d recommend this. I’ve recommended this to six or seven of my friends, and they’ve all hated me for it. The opening chapters start with talking about what happens when a heat wave passes the wet bulb maximum temperature that a body can take, so it starts with one of the most grisly chapters I think I’ve ever read. It’s set in India. They have a cascading power failure. The entire electrical grid goes down, which means they have no air conditioning. It results in a horrific scene, and this is what we’re trying to avoid.

If you want to be depressed, read this book, but it’s also a little bit of a positive one, where that catastrophe is then a galvanizing force for societies to come together and try to solve this. This was one of Barack Obama’s favorite books of the year, and I used that line to convince my friends to read it. They were like, “Oh, I like his book recommendations,” but then they all got mad at me and nobody finished the book because it’s just too brutal.

That’s it on the climate context.

The question we are going to be focused on is what can we do about it, and what specifically can we say about the solutions? You don’t need to jot this one down because we’ve already covered it.

We have a lot of solutions in place already that we saw in our micro-tools. Here I’m showing the basic one, and there is essentially consensus across the political spectrum of economists that the best solution would be to put a tax on carbon. Done. Period. Even conservatives agree.

That’s a problem, because we don’t seem to have the political willpower to do that. In a certain sense, there’s not a lot that we need to talk about. We know what the option is: put a tax in place that makes the marginal costs to society, including all these damages, equal to the marginal costs faced by the producers of carbon-emitting technologies. This would make coal much more expensive. That’s the basic mechanics of a carbon tax. That’s the solution.

But underneath this question is all sorts of complexity. When I say that we know the solution, we know the basic idea, but we need to figure out much more specifics, and that’s where it starts to get tricky.

The first thing we’ve already seen, and you don’t need to write this one down because we’ve spent time on this and it’ll be in your next assignment, is that there are differing opinions of how much we should care about the future versus the present. We’ve already seen this with respect to the discount rate. We saw how that changed optimal decisions, even in things like fisheries.

But it’s especially dramatic when it is applied to questions of climate change. The basic idea is that with a changing interest rate and a changing view of how far into the future you look will drastically change how much the values that you get in the future are going to be worth.

This one is really extreme. If you stood to gain $1,000 of value, someone will write you a check, but it’s going to be in 100 years, and you have a discount rate of 10%, which is pretty close to the market discount rate for investments, that is worth 7 cents. How much would you be willing to pay for this contract? You should pay no more than 7 cents.

In the climate context, what that means is almost any massively beneficial policy that might reduce climate damages is going to be worth very little today. That simply reflects the fact that the benefits of climate action are going to be felt by future generations.

If you do the math on this particular case, suppose climate change is going to cause $1 trillion in damages by the end of this century, by 2100. If you assume a discount rate of 3%, it would only be optimal, following cost-benefit analysis, to spend $7 billion to avoid that. I find that problematic, but that is just the basics of how standard cost-benefit analysis deals with time.

Those are the two challenges, but let’s pivot to what we can say. We’re going to talk about several Integrated Assessment Models, which I’ll be calling IAMs. Integrated assessment models are ones that combine some part about the environment with some representation of the economy. We’re going to talk about the most famous one: the Dynamic Integrated Model of Climate and the Economy.

We’re going to talk about this one for two reasons. Side note, we’re also going to call it the DICE model.

The first reason is it’s a really awesome economics model. It literally won its inventor, William Nordhaus, the Nobel Prize in Economics in 2018, which is pretty cool. As we’ll see, it draws directly on a lot of the material we’ve covered, namely the Ramsey model that we talked about in the past.

The second reason we’re going to talk about it is it’s also the basis for all sorts of climate skepticism from economists. We’re going to take it apart, but the short answer is the DICE model has been used to argue, using economic logic that’s pretty solid, that it is not optimal to spend all that much money on abating climate change.

This has caused all sorts of skepticism of the type that isn’t what we’re throwing out. This isn’t skepticism on whether it’s happening. It’s skepticism that says it’s happening, but it’s not optimal to spend very much on trying to deal with it. We are going to engage with that question very directly.

So what is the DICE model? It’s an integrated assessment model that links economic activity to climate change through the tried-and-true workhorse of economics: cost-benefit analysis.

It is going to be coupling a climate model that takes emissions from economic activity and uses intertemporal optimization, which says what’s the optimal amount of emissions and also the emissions abatement over time in order to maximize the net present value of consumption.

Have we heard this before? Yes, we saw that in the Ramsey model and the other models of economic growth. This is actually not that far off from standard macroeconomics. It’s going to use a growth model to look at the optimal growth and optimal decisions to maximize our happiness, but now with an extra twist where we can have the option to invest in climate mitigation, and the benefit of that is our future consumption might be higher because there’s less climate change.

That’s what the DICE model is. It’s a cost-benefit optimization that integrates climate and economy.

We will be talking about the most recent version, the 2023 version of the DICE model, but it’s worth mentioning that this has a long pedigree. The first model was from 1992. That was really when the first integrated climate-economy optimization model was put out, and it’s continued to be updated throughout the years.

William Nordhaus, the creator of this model, keeps adding updates to it, reflecting the fact that we have more years of observations and we see what countries have actually done, but also he keeps revising it to align with the latest climate information. For context, it’s this history of models that led to him winning the Nobel Prize.

The basic linkage here is going to be: we’ll have an economic model, which will cause two things to happen. Number one, it will affect the carbon cycle, tracking how much carbon is stored in the atmosphere, the upper oceans, and the deep oceans. He’ll keep track of those CO2 concentrations.

But then it will also calculate from those emissions what the amount of climate change is, and put these together into what’s called a damage function, which says how much loss to economic activity there would be as a function of the temperature.

I went through that quickly, so we’re going to break these down and talk about them in more detail.

What is the DICE model more specifically? It’s going to be really similar to what we’ve seen before. It’s an economist trying to maximize welfare, where our welfare is going to be the sum over all time periods out into the future of some sort of utility function, just like we saw before. That represents the idea that we get happiness from consumption. That’s the basic economics.

We are going to link it to population growth, because now we’re applying this to the real economy, not just a theoretical one, so we want to keep track of how many people there are.

And we’re going to add an important feature: the discount factor, RT. This is a simplification where we have the discount rate raised to a negative time power. This is the mathematical way that utility in time period 100 is going to be lowered by a very large amount, and it comes from this R factor.

The last term is just delta T, which allows us to think about time steps in the model, so we could look at the utility over time steps like every five years.

That’s going to be the maximization. Real straightforward, but the real twist is that this is going to be subject to a damage function.

In the Nordhaus style, the damage function, omega, takes uppercase T as its input. Lowercase t is time, uppercase T is temperature. It’s the simplest model in the world, almost, where it’s just going to be the temperature multiplied by some coefficient, plus the temperature squared times some coefficient. We’ll choose the values of these two parameters to try to match the damage function to studies that have tried to understand climate damages. We’ll spend a lot more time on exactly how that’s figured out.

The basic idea is that Nordhaus has a damage function that looks like this: at 1 degree centigrade, there’s going to be a tiny, well below 1 percent, reduction in GDP from that warming. At 6 degrees, we have a 19% loss of GDP, and you can see the curvature in there.

The final question is: what is the optimal amount of climate change to have? It’s going to ask what amount of temperature change will lower our utility from consumption in a way that is optimal, given the fact that climate adaptation and mitigation itself is expensive.

The coefficients are just calibration parameters. They are values drawn from the literature that make the shape of the function match empirical studies. Basically, Nordhaus looks at a bunch of studies in the academic literature and asks what values of pi-1 and pi-2 make this equation match the studies.

We’ll return to this, but where we’re going is that when you have this structure of maximizing welfare subject to damages, where you get to choose how much climate change happens, he solves for the optimal amount of climate change. What that represents is going to be expressed as the social cost of carbon, which we’ll get into in the next class.

Here’s the linkage. It’s the optimal climate abatement. When Nordhaus runs this model, it comes up with an estimate that removing one ton of carbon dioxide from the atmosphere is worth $51 of value.

The mechanics of this we’ll get into, but the challenge has been that this number is very low compared to what many scientists and other economists have argued is the optimal amount.

Why is that important? Because this number is used in cost-benefit analysis for what climate mitigation we should do. If you could reduce emissions for a really cheap price, less than $51 per ton, this is saying you should do it. But anything that would cost more than $51 per ton, you should not do, because it would be worse for everybody. The real problem is there’s not a whole lot that you can do to reduce carbon emissions at this low value of $51.

We’ll return to this and look at other contending models, but this is the skeptical baseline. At $51 per ton of carbon, the punchline of Nordhaus is that we shouldn’t do much.

That’s where I’ll leave it for today.

Thank you. Any questions?

Have a good Monday, and I hope you all have a good return from spring break.

Appendix

Climate Change as an Earth–Economy Problem

Learning objectives

After this chapter, you should be able to:

  • Explain why climate change is the archetypal Earth–economy problem.
  • Identify the climate system as a global stock–flow system.
  • Describe how emissions create a long-lived externality.
  • Compare the main climate policy instruments: taxes, permits, and standards.
  • Explain why partial-equilibrium thinking often fails for climate policy.
  • Describe how Earth–economy models integrate climate, land, and the economy.

Why climate is different

Many environmental problems are local:

  • a polluted river,
  • a depleted fishery,
  • a damaged forest.

Climate change is different.

  • Emissions anywhere affect people everywhere.
  • Damages unfold over decades and centuries.
  • The key variable—atmospheric CO₂—is a global stock.
  • The most affected people are often not the emitters.
  • Future generations have no voice.

This makes climate change the purest expression of the ideas in this book:

  • externalities,
  • stocks and dynamics,
  • public goods,
  • intergenerational tradeoffs,
  • and institutional failure.

It is the canonical Earth–economy problem.


Climate as a stock–flow system

Let:

  • E = annual emissions (flow),
  • C = atmospheric concentration (stock).

Each year:

C_next = C + E - natural_removal

Key features:

  • CO₂ persists for centuries.
  • Reducing emissions slows growth of C.
  • Net-zero emissions stabilize C.
  • Negative emissions reduce C.

Damages depend primarily on C, not E.

This creates a trap for intuition:

  • “We reduced emissions this year”
    does not mean
  • “The problem is shrinking.”

The problem shrinks only when the stock shrinks.


The climate externality

Each ton of CO₂:

  • raises global temperature,
  • increases extreme events,
  • affects agriculture, health, and ecosystems,
  • and imposes costs on others.

But the emitter:

  • pays for fuel and equipment,
  • does not pay for climate damage.

This is the textbook negative externality—
scaled to the entire planet and future centuries.

The economic solution is conceptually simple:

Align private incentives with social cost.

The practical implementation is politically and institutionally complex.


Core policy instruments

Carbon pricing

Two main forms:

  • Carbon tax: a fixed price per ton of CO₂.
  • Cap-and-trade: a fixed quantity of allowed emissions with tradable permits.

Both:

  • internalize the externality,
  • reward low-carbon choices,
  • let firms and households find the cheapest abatement.

Differences:

Feature Carbon tax Cap-and-trade
Control Price is fixed Quantity is fixed
Certainty Cost certainty Emissions certainty
Revenue Predictable Depends on permit price
Volatility Low Can be high

Both are tools for steering a complex system.


Standards and regulations

Examples:

  • fuel economy rules,
  • clean power standards,
  • building codes,
  • appliance efficiency requirements.

They:

  • bypass price signals,
  • target specific sectors,
  • can be faster to implement,
  • and often face less public resistance.

They are blunt but effective.


Public investment

Climate transitions require:

  • new infrastructure,
  • new technologies,
  • new skills.

Markets underprovide these because:

  • benefits are diffuse,
  • risks are high,
  • spillovers are large.

Public investment:

  • accelerates innovation,
  • builds networks,
  • and reshapes the feasible set.

Why partial thinking fails

A partial-equilibrium view asks:

  • “What happens in the electricity market?”

An Earth–economy view asks:

  • “What happens to land, food, trade, income, and ecosystems?”

Examples:

  • Biofuel mandates raise crop prices and drive deforestation.
  • Carbon taxes shift trade and production across borders.
  • Renewable expansion changes mineral demand and land use.
  • Adaptation alters migration and labor markets.

Climate policy ripples through:

  • land systems,
  • food systems,
  • energy systems,
  • and livelihoods.

Ignoring these feedbacks leads to:

  • leakage,
  • rebound,
  • regressive outcomes,
  • and political backlash.

Climate inside Earth–economy models

Earth–economy models integrate:

  • energy production,
  • land use,
  • emissions,
  • atmospheric stocks,
  • climate damages,
  • and economic response.

They can simulate:

  • policy pathways (taxes, standards, investment),
  • technological change,
  • land-use change,
  • and feedbacks.

Outputs include:

  • emissions paths,
  • temperature outcomes,
  • GDP and income,
  • land cover,
  • ecosystem services,
  • and inclusive wealth.

This allows questions like:

  • Does this policy reduce emissions but increase deforestation?
  • Does this pathway protect the poor?
  • Does this transition build long-run wealth?

Climate policy becomes a system design problem.


The Doughnut perspective

Climate overshoot pushes society:

  • beyond the ecological ceiling,
  • and often below the social foundation (through heat, floods, food insecurity).

A climate transition that ignores equity:

  • may reduce emissions,
  • but deepen social shortfall.

The Doughnut reminds us:

Climate policy is not just about carbon.
It is about building a safe and just future.

Earth–economy modeling is how we test whether we are doing that.


Open resources you can remix for this chapter

All are compatible with a CC BY-NC-SA Quarto book.

  • Natural Resources Sustainability: An Introductory Synthesis (CC BY-NC-SA)
    Use for: climate, sustainability framing.
    https://uen.pressbooks.pub/naturalresourcessustainability/

  • Principles of Economics (UMN Libraries Publishing, CC BY-NC-SA)
    Use for: externalities, taxes, public goods.
    https://open.umn.edu/opentextbooks/textbooks/principles-of-economics

  • InTeGrate teaching materials (many CC BY-NC-SA)
    Use for: climate data, mitigation pathways, policy exercises.
    https://serc.carleton.edu/integrate/teaching_materials/index.html


Exercises

  1. Stock vs flow.
    Explain why cutting emissions by 10% does not “solve” climate change.

  2. Instrument comparison.
    Compare a carbon tax and a clean electricity standard.
    Which gives more certainty? Which is easier to explain politically?

  3. System ripple.
    Choose one climate policy.
    Describe two indirect effects it could have outside the energy sector.


Chapter roadmap

  • Next, we examine uncertainty, risk, and tipping points.
  • You will see why climate change challenges standard cost–benefit logic and demands new decision frameworks.