DICE and Climate Skepticism
It all depends on the social cost of carbon
Resources
Slides 17 - Future Scenarios and SSPs
Content (Day 1)
Welcome to Chapter 17, Lecture 26: Future Scenarios and the SSPs
Introduction and Opening Remarks
Welcome to Chapter 17, Lecture 26 of this course on environmental sustainability and ecosystem services. The numbering system for lectures has become somewhat flexible, but today’s focus is on future scenarios and the SSPs, including what that acronym stands for. This lecture combines theoretical frameworks with practical applications to help understand how we might approach global sustainability challenges.
The Lecture’s Visual Foundation
Artificial Intelligence Art and Course Imagery
The artificial intelligence art generated for this lecture is particularly effective at conveying the core message. By inputting the lecture title and asking the AI to create an image for this class’s content, ChatGPT 5.4 has become increasingly familiar with both the instructor’s teaching style and the course’s overarching themes. The resulting image illustrates a striking transition from an Armageddon-like world characterized by industrial smokestacks, planes, and overall scariness, to a beautiful and hopeful vision of a sustainable future where supply and demand change dynamically over time in positive ways. While the image misses some of the nuance inherent in complex sustainability discussions, the left side of the image effectively captures the dystopian concerns that motivate much environmental policy. The right side represents aspirational goals that many in the sustainability field hope humanity can achieve.
Course Administration and Logistics
Country Assignments for Weekly Questions
The first item of business involves country assignments related to the weekly questions that students submitted the previous week. The instructor has carefully made these assignments based on student preferences. The process will involve discussing which students received their first-choice countries and which did not, as well as outlining the procedure for petitions and reassignments.
The Assignment Algorithm and Preference System
Every student was asked to list four country preferences, which in retrospect may have been overkill in attempting to minimize non-first-place rankings. The rationale behind collecting multiple preferences was to allow the instructor to use an algorithmic approach to optimize assignments across the entire class. The instructor employed an algorithm specifically designed to minimize off-by-one errors and suboptimal assignments. Upon the first pass through the algorithm, the instructor found that four people did not receive their preferred assignments. However, through strategic swaps and recalibrations, the instructor determined that this was actually the optimal outcome—the algorithm could not achieve a better result.
Petition Process for Reassignments
For those students who did not receive their first-choice country assignment, the instructor is allowing them to send a petition email making their case for an alternative assignment. Specifically, if students are named Gabriella, Alex, or Kayla, they are invited to email the instructor with their preferences. If no petition email is received by the end of the class day, the instructor will proceed with a random assignment from the remaining available countries. Students should reference the assignment slide to identify which countries are still available and to avoid claiming a country that has already been assigned.
Upcoming Assignment Deadlines
A reminder for all students is that Assignment 3 is due tonight. This assignment corresponds with the micro quiz scheduled for Friday. After the midnight deadline—realistically sometime on Tuesday morning—the instructor will post the answer key so students can review the correct mathematics and problem-solving approaches behind each answer.
A Personal Digression: New Computer Acquisition
Before moving into the main content, the instructor shares a personal anecdote about obtaining a new computer. For years, the instructor has operated under a personal rule with his wife that he is only allowed to purchase a new computer if a student beats his computer in an in-class exercise. This has not happened in the current class, but in another class the instructor teaches, students have been beating his computer while running ecosystem service models. At first, experiencing these losses felt somewhat shameful because the instructor identifies as a computer person. However, he was also excited about the possibility of upgrading his equipment.
After much deliberation about computer performance issues—PowerPoint crashes and general slowness that had been wasting collective class time—the instructor decided to make a major leap from Windows to Mac. Apple’s chip technology has improved dramatically in recent years. While Apple chips used to be valued primarily for their energy efficiency and user-friendliness, they are now both more energy efficient and faster than competing processors. The instructor acquired one of the latest benchmarks on the Apple M5 chip, representing the most powerful option available at the time of purchase. This upgrade is anticipated to significantly improve the instructor’s ability to teach and present course materials effectively.
Introduction to IPBES and Its Significance
Defining IPBES and Its Purpose
Now turning to content more directly relevant to the course, the discussion moves to IPBES. While this might seem like somewhat of a transition from the previous administrative and personal remarks, understanding IPBES is essential to comprehending the frameworks that will be discussed throughout this lecture. The instructor acknowledges that he will not write out the full name of the organization because of its length, but the complete title is the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Clearly, the organization’s designers were not particularly skilled at creating concise, memorable acronyms.
Essentially, IPBES functions as a large convening body that brings together the current state of scientific knowledge on biodiversity and ecosystem services. The easiest way to explain what IPBES does and why it matters is to understand it as the IPCC equivalent for nature. Just as the IPCC synthesizes knowledge about climate change, IPBES synthesizes knowledge about biodiversity and ecosystem services.
The IPCC and Its Historical Context
To understand IPBES better, it helps to understand the IPCC first. The IPCC, or Intergovernmental Panel on Climate Change, is a much older body that was established in the 1980s. It won the Nobel Peace Prize, which it shared with Al Gore, a fact that the instructor found somewhat amusing. The IPCC functions as an organization of different nation representatives who work together to synthesize everything that is known about climate change and to define what constitutes scientific consensus on the topic.
Despite considerable scientific debate on various aspects of climate change, the IPCC serves as an important authoritative body. It is represented by the best available scientists and country delegates who negotiate and determine what exactly constitutes consensus science on climate and climate change. This process of synthesis and consensus-building can be contentious, but the IPCC provides a valuable institutional framework for bringing together diverse perspectives and establishing what we can say with confidence about climate science.
IPBES as the Nature Equivalent to the IPCC
IPBES operates according to similar principles as the IPCC but with a different scope. Rather than summarizing science on climate change alone, IPBES looks more broadly at biodiversity and ecosystem services. It is a large organization that is hosted by the United Nations and currently involves 141 member country delegates. It is worth noting that the United States is no longer a delegate to IPBES. Approximately one month before this lecture was recorded, the current U.S. administration formally removed the United States from IPBES participation. However, the United States was part of the organization previously and will likely be involved again in a few years as political leadership changes.
IPBES Assessment Reports and Research Outputs
IPBES synthesizes existing scientific knowledge about biodiversity and ecosystem services into major assessment reports. These reports focus on specific topics of significant policy relevance. Examples of IPBES assessment topics include the sustainable use of wild species, values—how we value nature—and business and biodiversity. One noteworthy detail is that Steve Pulaski, co-founder of the instructor’s NatCap research center team, currently serves as the chair of the writing group for the business and biodiversity report. He is extraordinarily busy in this role. When IPBES released a press release about this report, it generated approximately a thousand media hits almost immediately, demonstrating the significant public and media interest in these assessments. These are major reports that many people care deeply about.
The IPBES Conceptual Framework
In addition to putting out comprehensive reports and defining consensus on biodiversity and ecosystem services, IPBES does an excellent job tying together different frameworks for understanding how humans and nature relate to each other. The IPBES conceptual framework, which was published as a major document, provides one such integrative approach. While it is not necessary to go into all the details of this framework, there is one key aspect that deserves particular attention: IPBES operates as a negotiating body.
Every report that IPBES produces requires buy-in through a consensus system from all 130-plus member states participating in the organization. This consensus requirement also includes Indigenous communities and non-Western knowledge systems. This has proven to be quite challenging because there are a variety of different ways of describing nature and how it relates to humanity.
Different Conceptual Framings of the Human-Nature Relationship
The language used to describe nature and humanity’s relationship to it varies significantly across cultures and perspectives. In many Western scientific contexts, we simply use the word “nature.” There are also science-specific terms like “biodiversity and ecosystems” that are used in technical discussions. However, many other perspectives on nature exist. Some cultures and perspectives speak of “Mother Earth,” “systems of light,” or refer to nature’s “intrinsic value.” These are fundamentally different ways of conceptualizing what nature is and what our relationship to it should be.
The different member states of IPBES have engaged in fascinating and sometimes contentious debates about what the relationship between humans and nature really is. Very Western scientific views tend to characterize the relationship in terms of ecosystem services—there are production functions and ways that nature provides value to humanity. However, other countries, such as Ecuador, have argued that this framing is not correct or sufficient. From this alternative perspective, we are part of nature in a gift-giving relationship, which is fundamentally very different from the idea of ecosystem services that characterize nature as something we are extracting value from.
Three Different Framings of Nature’s Value to Humanity
Over the course of these discussions and negotiations, at least three different phrases have emerged for approximately the same set of concepts. “Ecosystem goods and services” is the framing that the instructor emphasizes throughout most of the course, given his own Western scientific training and background. This is the dominant way of thinking about human-nature relationships in much of Western environmental economics and policy.
A middle ground between very Western economic framings and more culturally diverse perspectives is the phrase “nature’s contributions to people.” This alternative framing flips the agency in an important way. Rather than emphasizing that we are extracting or taking from nature, this framing emphasizes that nature is actively contributing to us and our wellbeing. Despite this different framing and different emphasis, “nature’s contributions to people” essentially points to the same set of ecological functions and benefits as the ecosystem services framework.
Finally, the phrase “nature’s gifts” moves further away from the Western scientific and potentially economically narrow vision of the human-nature relationship. This framing emphasizes the gift-like or inherent value of nature rather than focusing on instrumental values and services provided to humans. It represents a different philosophical stance on the relationship between humanity and nature.
Personal Philosophical Background and the Evolution of Environmental Thinking
As a brief aside, the instructor notes that during his undergraduate years, he was a philosophy major for quite some time and was deeply fascinated by the fundamental question of what the right way for humans to live with nature is. At a very deep level, he identified as an environmentalist, but initially, this environmentalism was not grounded in economic thinking. Instead, he wondered about basic questions such as whether everyone should live in natural huts or whether there is something fundamentally problematic about living surrounded by air conditioners and concrete. He spent considerable time thinking hard about environmental ethics and the principles that should guide human behavior toward the natural world. Environmental ethics remains a fascinating topic.
However, the instructor’s intellectual journey led him in a different direction. He eventually moved away from primarily focusing on environmental ethics and philosophy when he realized that this approach did not seem particularly persuasive when engaging with people who disagreed with environmentalist perspectives. He did not really like economics at the time and saw it as part of the problem—as a system fundamentally oriented toward the exploitation he wanted to challenge. But over time, he discovered that economics is actually very persuasive. People who might resist purely ethical arguments about environmental protection are often responsive to economic arguments about efficiency, valuation, and cost-benefit analysis. This realization is precisely why the instructor now emphasizes economic approaches, particularly ecosystem services, in his teaching.
That said, the instructor maintains that while we will talk extensively about ecosystem goods and services and their economic valuation throughout this course, there remains a much deeper philosophical question about what relationship humanity ought to have with nature. This deeper question is one that may be very different from the relationship we have right now, and it deserves ongoing consideration even as we engage with economic frameworks.
The IPBES Conceptual Framework and Systems Integration
The IPBES conceptual framework also describes many other important spectra that matter when analyzing the human-nature relationship and how it changes over time. The framework emphasizes thinking about both global and local scales and how things change over time. IPBES emphasizes wanting to understand how different drivers—ranging from natural drivers such as solar variation and changes in atmospheric circulation patterns to anthropogenic drivers such as carbon emissions and land-use change—relate to the provision of nature and the value we get from it.
Critically, the framework focuses on how human systems, institutions, and governments affect these relationships and drivers. This conceptual framework provides a comprehensive way of thinking about all these interconnected things—drivers, ecosystem processes, human institutions, and value generation—as one combined system rather than as separate domains.
Scenarios as a Framework for Future Analysis
The Crucial Role of Scenarios in Understanding Future Pathways
One of the most specific and important things that came out of the IPBES framework was the recognition that creating different scenarios of future livelihood is a crucial step in understanding how to analyze humanity’s connection to nature over time. This recognition led directly to the development and popularity of scenario analysis in the sustainability field. Therefore, the discussion now moves to scenarios and scenario types.
Four Types of Scenarios in Policy Analysis
The IPBES framework emphasizes four distinct types of scenarios, each serving a different purpose in the policy-making and planning process. To understand these four types, it helps to visualize them on a diagram that shows the policy-making process as a cycle. The policy-making wheel going around the outside shows different parts or phases of the process that different types of scenarios address.
Type 1: Exploratory Scenarios and Agenda Setting
The first type of scenario is exploratory. Exploratory scenarios appear on the left part of the policy-making cycle diagram. When talking about policy and planning, the beginning phase is often agenda setting. During agenda setting, stakeholders and policymakers do not yet know exactly what goals they want to achieve, so a good way to start the process is to explore the full possible space of futures. Exploratory scenarios serve this purpose.
To understand exploratory scenarios and all scenario types, it is useful to draw a simple graph with the value of nature—ecosystem services or nature’s contributions to people—on the vertical axis. This vertical axis represents the amount of value that humans get from nature. On the horizontal axis, we have time, with three key points marked: the past, the present, and the future. This is a fundamental way of structuring how we describe time and how conditions change through time.
For exploratory scenarios specifically, the graph shows an observed era that extends up to a decision point in the present. Beyond that decision point lies the future, which we do not yet know because it has not happened. Exploratory scenarios examine different possible outcomes without necessarily saying what factors or policies are driving them. What happens if this type of climate change occurs? What if human behavior causing deforestation continues unchanged? What if we dramatically reduce consumption? These exploratory scenarios explore all possibilities in an open-ended way. They are used for exploring what different targets or states we might find ourselves in by 2050 or 2100, or whatever time horizon is relevant.
This stage is inherently complex, and people often disagree significantly. If we cannot agree on the definition of the nature-human connection as discussed earlier, people will definitely disagree on which scenarios we want or prefer. But this exploratory phase is where the agenda is initially set, where we begin to collectively identify what futures are possible and which ones might be desirable.
Type 2: Target-Seeking Scenarios and Policy Design
The second type of scenario is target-seeking scenarios, which relate to the next step in the policy-making process. Ideally, after agenda setting in phase one, there is an agreement on what outcomes are desired. Target-seeking scenarios serve the policy design phase that follows this agreement. These scenarios have a slightly different structure than exploratory scenarios.
The first part of the target-seeking scenario graph is the same as the exploratory graph—we have the same observations showing that nature’s value has been going down over the historical period. What is different and crucial is that we now have a goal, represented as a diamond or star on the horizon in the future. Once this goal is set as our objective, target-seeking scenarios explore and evaluate different possible routes or pathways to get there. The emphasis is on policy design. We now have a concrete target we are seeking—what are the best ways to design policies to achieve that target?
Target-seeking scenarios are hopeful in their orientation because they assume we know where we want to go and that we can design policies to hit that target. If we identify an end state we want to achieve—for instance, healthy ecosystem services globally by 2050—we then work backward to figure out what policies and actions would make that possible. However, the third type of scenario represents a more realistic characterization of how governments actually proceed.
Type 3: Policy Screening Scenarios and Implementation Reality
The third type of scenario is policy screening, which represents a more realistic approach to how governments and large institutions actually approach the challenge of moving toward desired goals. Policy screening shares the basic idea that there is a goal that we want to hit and that we should design policies to achieve it. However, policy screening introduces a crucial dose of realism: we do not get to play God and make the world exactly how we want it to be.
In policy screening scenarios, the lines connecting current conditions to the desired future target do not necessarily lead in a straight line right to the target. Instead, policy screening focuses much more on the realities of implementation. We are working with imperfect policies in the real world. One policy may overshoot the target, another may undershoot it, and it is genuinely hard to figure out whether any given policy even succeeds at getting us to our objective because of complexity and unintended consequences.
Policy screening is much closer to how governments actually think about implementation and policy design. Rather than imagining an ideal world, policy screening asks: what are actual policies we could write? Maybe one policy is setting a carbon tax at a certain level. Another might be setting caps on carbon emissions from coal-fired power plants. These represent the kinds of actual laws and policies that we might write and pass, and policy screening asks whether and how well these real-world policies succeed at getting us to our stated objectives.
Type 4: Retrospective Scenarios and Policy Evaluation
The fourth type is retrospective or policy evaluation scenarios. Retrospective scenarios are exactly what the name suggests. Rather than focusing on the future part of the graph, retrospective scenarios focus on the past. We observe a historical period where we did implement a policy and we try to estimate the implementation gap—how much we missed out on because we did not take action or because our actions were ineffective. This retrospective analysis establishes how well we actually did.
This retrospective analysis is important for evaluating whether the cost-benefit analysis or other projections we originally calculated for a policy were actually correct. A lot of current economics focuses on this retrospective type, doing statistical analysis to determine whether a policy had the causal impact that was predicted. Did a carbon tax lead to the emissions reductions it was supposed to? Did a protected area designation actually preserve biodiversity as intended?
However, this is not the end of the cycle but rather the beginning of the next cycle. When you do retrospective analysis and learn something about implementation, what you learn feeds back into the next phase of decision-making. The new data and insights from retrospective analysis help you better assess what options you might want in the next round of agenda setting. It is an iterative process where each phase informs the next.
The Broader Application of the Scenarios Framework
The instructor finds this scenarios framework useful far beyond the environmental realm. If you are a business analyst, it is literally the same process. You do market research—that is agenda setting. You ask what products might we produce and what markets exist? Then you move to: okay, we want to be in this market. What is the best way? What manufacturing policies might we employ? Then, the retrospective phase: did we do a good job? Did our strategy work?
Being able to discuss this scenarios framework with a CEO or someone you are in a debate with is a really effective way of honing arguments and clarifying thinking. This is because people talk about different scenarios all the time, but they are often talking about different phases of this cycle. Some people are in agenda-setting mode talking about possibilities, while others are talking about policy implementation. Some are focused on retrospective analysis of what happened. When everyone is clear about which phase of the scenario process they are discussing, the conversation becomes much more productive and clear. Knowing where you are in this cycle really helps.
Personal Experiences with Scenarios in Research and Practice
Graduate Research and Initial Misconceptions About Scenarios
The instructor shares a couple of stories about how scenarios have been massively important throughout his career and how his understanding of scenarios has evolved. The first story comes from his PhD and postdoctoral research. The instructor got a postdoc where he was supposed to study how different countries incorporate ecosystem services into their sustainable development goals. He was specifically charged with being the scenarios guy for the project. This meant he had to give a presentation at a professional level to a group of PhDs on what scenarios are and how to use them.
At the time, the instructor had not seen all the different approaches to scenarios in academic literature, and he only thought of scenarios in the policy screening space. He had written one research paper discussing scenarios from this perspective. He gave what he now recognizes was an awful presentation to the assembled group of experts. Basically, he said the following: here is one map of what the world looks like if we did Policy A, and here is another map if we did Policy B. According to his initial framing, scenarios are just about defining these maps accurately and conducting precise modeling. What is the big deal about scenarios?
Well, what the instructor learned later was quite a shock. Approximately 90 percent of the people in that group were focused on the agenda-setting side of the scenarios cycle. They were social scientists who did not particularly like numbers and quantitative models. They preferred narratives. They spent their days talking with local residents, farmers, different community leaders, and other stakeholders, trying to elicit narrative descriptions of what future world people wanted to live in. Instead of maps and numbers, they were deeply embedded in their communities, thinking about justice, value, social systems, and cultural preferences. They were genuinely appalled that the instructor was over-quantifying the scenarios and missing what they saw as the key point: scenarios are really fundamentally about meeting with stakeholders, asking what they want, and describing their preferences in narrative form. Maybe eventually the process gets into a map or quantitative model, but this group of social scientists saw the instructor’s quantitative results and conclusions very differently than he did and seemed to think he had missed the entire point.
The World Bank Experience and the Limits of Optimization
The second story the instructor shares is almost the complete opposite of the first. Later in his career, the instructor had written a paper that moved into the policy screening space where he tried to find what he believed was the optimal policy for achieving a desired world state. Instead of presenting two different maps showing different policy scenarios, he had created a single map showing what an optimal world would look like. This map indicated where agriculture should be, where forestry should be, and where other land uses should be allocated. The instructor had looked at the most cost-effective way of getting to this ideal state through analysis and optimization.
When the instructor and his collaborators presented this work, the World Bank expressed interest in potentially funding them because they liked what the researchers had done and the approach taken. However, when the instructor talked to World Bank representatives about next steps, he explained what the research really showed: here are the different policy options that could potentially get us there. The World Bank representatives looked at him and asked a simple but profound question: “Do you think you can play God?” This is where that particular phrase and challenge comes from.
The World Bank representatives explained that you cannot simply decide what the landscape should look like. That is not actually a policy that a lawyer could read and implement. It is not something that could be written as an actual law or enacted as a formal policy. They were interested in policy screening, not in optimization of the ideal world. They wanted something that could be written as an actual law, a loan decision criterion, or a policy statement that a lawyer could draft. As a bank, what the World Bank does is make loans to different countries for major development projects like dams that have significant environmental impacts. They wanted to know ways they could change their policy—their loan-making procedures—in terms and language that takes into account and values nature appropriately. They wanted specific policy mechanisms they could implement, not a vision of an optimized world.
Lessons Learned from These Experiences
The instructor has had a lot of learning through these kinds of interactions and exchanges. The next learning experience, he notes, will likely come from deeper engagement with the fourth type of scenarios: retrospective or policy evaluation scenarios. Retrospective scenarios are, as discussed, exactly what they sound like—we focus on the past and we observe a historical period where a policy was actually implemented. We try to estimate how much we missed out on compared to our original goals or predictions, which helps us establish how well we actually did compared to our initial projections.
Logistical and Technical Notes for the Course
Access to SSP Data and Resources
Before breaking for the day, the instructor wants to highlight two mechanical and logistical things that will be important for the remaining portion of the course. Number one is an explanation of the specific reason the instructor likes SSPs. The reason is that SSPs are consistent across applications, quantitative, and have a comprehensive database. It is a super useful database. The instructor has put a link in the course materials, which may be super useful when students do their country reports later in the course. Through this database, students can get access to all these predictions for the different scenarios under different SSP pathways in downloadable format that is useful for reporting on a particular country’s sustainability prospects.
Installing and Preparing QGIS for Future Class Sessions
Number two is an important software requirement. The instructor wants all students to install QGIS before the next class. The instructor will send a link via announcements after this class, but for reference, the website is qgis.org. QGIS is not too hard to install or set up. This is a tool that the class will use multiple times throughout the rest of the semester for geographic information systems and spatial data analysis. The software is free, it is awesome, and it works on Windows, Mac, and Linux operating systems. When students download it, they should get the latest release. All students in the class will use the 4.0 release. The software is a fairly large file, which is why the instructor is assigning it as homework to install in advance. The objective is to get students started the installation and download process early rather than having everyone trying to download it right before class, which would cause network congestion and swamp the wireless systems at that time.
After installing QGIS, students should make sure they can open it and that it runs on their computer. If there are problems or errors when opening QGIS, students should test it and debug it before the next class. The class will dive right into using QGIS in the next session, so it is crucial that the software is already installed and functional.
Closing Remarks and Encouragement
The instructor closes by asking if there are any questions about the scenarios framework and SSPs. The class has been covering these topics that underlie the Shared Socioeconomic Pathways, though many concepts were not presented at the beginning because there would not have been enough foundational information to make everything make sense. Now that the concepts have been presented, the framework should be much clearer. The instructor notes that the class is getting more hands-on in terms of tools and techniques, and it is going to be fun. The instructor really likes spatial data, so that will be evident in the remaining lectures. He wishes everyone a good rest of their Monday.
Content (Day 2)
Lecture Two: Scenarios and Land Use Change
Opening Remarks and Logistical Updates
Welcome to day two of the course, where we will be finishing up the Shared Socioeconomic Pathways (SSPs) and discussing the narratives component that has not yet been covered. Before diving into the content, there are several logistical notes to address for the class moving forward.
Classroom Relocation
Next class will be held in a different classroom located in Rattan Hall, room 135B, which is in the same building but a different space. An announcement will be sent out to inform everyone of this change. The classroom is being changed because it has much better seating arrangements that will accommodate the interactive computer work that will comprise a larger portion of the remaining semester coursework. While it is unfortunate that this computer-focused work cannot begin immediately in the new space, the instructor will be circulating around the classroom to help ensure everyone is on the right pages and accessing the correct resources. The room has been reserved for the rest of the semester, so it is expected that every class will be held there, though the course can return to the current location if the new arrangement does not work well for the class. The instructor prefers the new room and will be taking opinions from students on the arrangement.
Software Installation and QGIS
The instructor plans to pick up where the class left off on the SSPs and discuss the associated narratives, move into a group exercise that will be specific to each country, and then switch over to slides on land use change that are already posted online. If time permits, the class will begin using the software that students were asked to install over the previous few days: QGIS. The instructor asks for a show of hands to determine how many students successfully got QGIS up and running on their computers. The response is quite positive, with most students having successfully installed it. The instructor explains that the download was particularly slow because QGIS had just released version 4.0, which had been long awaited, and their servers were swamped with many people downloading the new version simultaneously, making the process much slower than usual.
Understanding SSPs: The Framework
The Two-Axis Framework
The instructor wants to dive right in and pick up where the class left off with the SSPs. To establish context, the instructor quickly reminds the class of the key plot that has been used to organize the SSP scenarios. The scenarios are defined by two axes: Adaptation Challenges and Mitigation Challenges. These are two key terms that require clarification. Mitigation refers to keeping CO2 and other greenhouse gases out of the atmosphere to prevent climate change from happening in the first place. Adaptation, by contrast, means acknowledging that climate change will happen regardless and figuring out how humanity can adapt to those changes.
Within this framework, SSP2 represents the middle-of-the-road scenario, SSP1 is the good scenario, SSP3 is the worst scenario, and then SSP5 and SSP4 complete the framework. This framework is referred to as the challenge space by some of the earliest articles put out by the IPBES and the IPCC.
Data and Narratives Behind the Scenarios
The instructor has already made the point that these scenarios are defined by really detailed data on projections of GDP, population, land use, and other critical variables. However, what lies underneath the surface of these detailed projections? These are the officially recognized narratives, which consist of short paragraphs put together by expert communities to define what each scenario is supposed to illustrate. Understanding these narratives is critical because they provide the conceptual underpinning for all of the detailed data projections that follow.
The Five SSP Scenarios: Detailed Analysis
SSP1: The Green Road
SSP1 is called the Green Road scenario and is characterized by several key components that work together to create an optimistic environmental future. Land use is strongly regulated in this scenario, which is one of the most important aspects of defining the future beyond just climate change. Strong land use regulation denotes that tropical deforestation rates are going to be greatly reduced compared to historical trends.
Simultaneously, this scenario assumes that crop yields are rapidly increasing, especially in low- and medium-income countries where such increases would have the most impact on deforestation patterns. There is a tight relationship between these two statements that is worth exploring. The reason these are connected is that if crop yields increase substantially, farmers do not need to clear additional land to maintain or increase production. A whole lot of work is going into research and development to see how we can increase crop yields, with the presumption that higher productivity will reduce the pressure to convert forest to cropland. This is an appealing option because it represents a win-win scenario where farmers benefit from higher yields and there is a positive environmental outcome. Farmers like it when their yields go up and generally resist being told not to farm their farmland or that they cannot convert a patch of forest into farmland. The win-win nature of this approach means that crop yields can go up while simultaneously achieving positive environmental outcomes through reduced deforestation.
A related point is that healthy diets are assumed in SSP1, with much lower animal calories as a share of all nutrition consumed by the average person. This dietary shift is also related to land use patterns. The connection between changing towards lower meat diets and reduced cropland expansion is significant. If we are getting our calories from beef, we have to think not just about the land on which the cow grazed, unless the animal is fully grass-fed which most are not, but also about the land necessary to grow the grain that feeds the cow. For cows, it requires between eight and ten times more land to grow the grain needed to produce beef than you would need to get the same amount of calories if you simply ate the grain yourself. The human digestive system is not very efficient when converting meat calories to nutrition, so if we want to make calories available in the food system, we need a lot more land if those calories come in the form of meat than if we had simply eaten the underlying grain ourselves.
Land use emissions are assumed in this scenario to be priced at the level of carbon prices in the energy sector, which is essentially a long way of saying that there is a carbon tax. Importantly, this carbon tax is not just applied to easy targets like coal-fired power plants, but it also applies to land-based emissions. These are the key factors that make SSP1 good. Mitigation comes out favorably here because there is less land being converted from carbon-rich sources like forests to cropland. The mitigation challenges will be relatively low, but also adaptation challenges are relatively low because we are effective at increasing yields and adapting to changing conditions. So both mitigation and adaptation are relatively manageable in this scenario. There are many more variables that go into the detailed definition of SSP1, but these are the key components that the class will focus on.
SSP2: Business as Usual
SSP2 is the middle of the road, which is just another way of saying business as usual. In this scenario, development and income continue to proceed the way they are, which remains really unequal globally. Specific to the key variables that define the SSPs, we are going to have a less optimistic world where crop yields increase, but the rate of increase is declining over time.
This is a scary situation because we are currently in a world where we have been experiencing massive, unprecedented increases in crop yields, and much of the food security system depends on this trend continuing. But there is strong and growing evidence that the rate of yield increases seems to be plateauing. This is a significant problem because a lot of our projections for future food security depend on yields continuing to increase indefinitely. The University of Minnesota is particularly famous for some of the research on this topic. Way back in 2013, the university had one of the first papers to really look at global statistics at a gridded level on where yields were slowing their rates of improvements.
This scenario assumes that instead of a shift towards plant-based diets, caloric consumption and animal calories converge towards high levels. China, for instance, which has traditionally consumed less meat per capita, basically catches up to the level of meat consumption in the rest of the world. The same thing happens with India and other developing countries as they converge towards the diet of high-income countries.
The result is that deforestation is not really incentivized until later on in the scenario. There is a time element to this middle-of-the-road scenario. Not until 2030 do we start to have effective policies for preventing deforestation. This is the most boring scenario because it is what would happen if we just keep doing what we are doing. However, the framework also allows us to look at a variety of other scenarios beyond this middle road.
SSP3: Rocky Road
Let us go to the worst scenario: SSP3, called Rocky Road, which the instructor loves as an ice cream flavor but would not prefer as a road to drive on. This scenario is characterized by several things, but the key one is resurgent nationalism. When SSP3 came out, quite a while ago in 2012, a lot of people disregarded this scenario, wondering whether it was really that important to consider scenarios with resurgent nationalism. Many felt that the world was inevitably heading towards more international negotiation and trade. But yeah, this scenario has become much more relevant. There has been a marked increase in nationalistic policies pursued here in the United States and throughout the world.
Why is resurgent nationalism important from a sustainability lens? The answer is that countries will be competing with each other instead of working towards a common goal of solving climate change. The idea of free-riding makes more sense in this context. If you only care about your one country and think all the countries around you will not cooperate, it increases the case that you should just protect your own interests and free ride on everybody else’s efforts, rather than waste money solving a problem for other countries you hardly benefit from.
This is a challenging scenario, and it will have divergence in addressing ecological and economic inequality over time. In terms of land use, land will be hardly regulated. You can sort of do whatever you want with your land. Rates of crop yield increase will decline over time, but especially badly in developing countries. This is a key difference from SSP2, where developing countries are closing the gap in many things. But what if we have a nationalistic, isolationist approach? There will be less trade. There might be less sharing of technology and investment from high-income countries into low-income countries that would facilitate the uptake of new agricultural technology. These scenarios will have very limited transfer of new agricultural technologies to developing countries.
This scenario will also have unhealthy diets with high animal shares and high food waste. Currently, we waste a little under fifty percent of our food globally. Some people say that food waste reduction is one of the most effective levers we could pull to minimize the environmental impact of our food consumption. However, the instructor is really skeptical of that argument because they cannot figure out any good policy mechanisms to effectively reduce food waste. What policy would encourage a buffet to have smaller amounts of food? Raising the price on food would be one way, but the challenge with this approach is that it is one of the few things where raising the price might have especially negative consequences. If we raise the price on luxury cars, no big deal and few people are affected. If we raise the price on food, that might have real knock-on impacts on affordability for vulnerable populations. But this is basically the argument we use for other things, like putting a carbon tax on food based on how much carbon it contains or on waste. The instructor could be persuaded by this argument, but it is one of those problems that is harder to solve in practice. The instructor really cares about the environment, and yet they waste food. They have a hard time not accidentally buying the wrong amount of groceries and having food go bad in their refrigerator. They wonder if anybody else has this problem.
A key aspect of SSP3 is that with regional rivalry and the focus on nationalism, there will be much less trade. A lot of environmentalists might cheer this option, thinking that trade is bad for the environment in general. Well, in this world, it is quite the opposite. If you have trade, you can allow high-efficiency agricultural-producing countries to trade their food to less efficient ones, and this reduces the total cropland needed to feed the world. If we did not have this trade on agricultural goods, each country would have to be self-sufficient and produce more locally, even if they are not very good at producing all the different foods. For instance, the United States is really good at producing corn and wheat, but not so good at producing bananas. If we focus on self-production, we will be achieving lower productivity outcomes overall because each country will be forced to use inefficient production methods for crops that do not grow well in their climate.
So SSP3 has that rocky road character that defines it. SSP4, however, turns it up to eleven in terms of inequality.
SSP4: Road Divided
SSP4 is called Road Divided and is characterized by tons of inequality, but you will not have the bad mitigation challenges that come with SSP3. This would be one where we have highly unequal investments in human capital, and this leads to some good things like greater emphasis on low-carbon energy sources. It solves part of the climate problem, but there is very little development of technology in low-income countries.
We get a divergence where we would have highly effective regulation of land use and deforestation in high-income countries, but in low-income countries that tend to be tropical, there will be relatively less prevention of agricultural expansion. The question of why low-income countries tend to have tropical ecosystems and what that means for environmental protection is worth exploring. What do we know about tropical areas? They are characterized by rainforests and their extraordinary biodiversity. This is something we will be coming back to much more throughout the course. But it is an awful twist of reality that the countries with the lowest incomes also tend to have the richest endowment of biodiversity, of rainforests, and really important ecosystems.
So not having land expansion regulated in tropical, low-income countries is especially bad from an environmental perspective. If we have deforestation in Minnesota, that is bad. We lose a few white-tailed deer or something relatively common. But if we have it in places that are rainforests, these places have ten times as many species of animals present, and many are endangered. So for a given hectare of deforestation, it is much, much worse if it happens in low-income countries on average because the species density and uniqueness of tropical ecosystems is so much higher.
This divergence also comes out in crop yields. High-income countries continue to increase their yields through investment and technology adoption, but low-income countries do not. There is a convergence towards a medium level globally, and food trade remains globalized. But access to these markets will be limited in low-income countries. The ability to buy fertilizer in Sub-Saharan Africa is quite challenging. There are not sufficient markets that provide fertilizer, so it is either very scarce or very expensive, and as a result, not used very much. This limits the ability of low-income countries to increase their crop yields even if they wanted to.
SSP5: Fossil Fuel Highway
The last scenario is SSP5, called Fossil Fuel Highway. This world has good things and bad things. It will be one that emphasizes fossil fuels but results in rich societies overall. So what if we continue our basic emphasis on competitive markets, rapid innovation, and technology advance, and basically burn a lot of fossil fuels to make ourselves as rich as possible?
For mitigation, it means we have a ton of challenges. We are going to have a ton of climate change, and this is the SSP typically paired with RCP 8.5, which represents really bad climate change. But what this also shows is that there are benefits, and that is why being an environmentalist is sometimes quite complex. If we get really rich off fossil fuels, we could spend that on adaptation. Everybody could have good air conditioners, or we could have really good public health systems. That is a big assumption, though, and assumes that we would get rich and spend it on those things. If we got rich and did not spend it on adaptation, we would be in a much worse position, right? But it is a possible future where we continue to have increased climate change but spend money on dealing with the damages, especially for those most at risk.
In terms of land use, it is incompletely regulated. Tropical deforestation continues, although it is assumed to slowly be declining over time. But what do we get from our investment in burning our stock of fossil fuel capital? Crop yields going up across the board. This would be a world where we put tons of fertilizer everywhere, successfully solving the missing markets for fertilizers in Sub-Saharan Africa, and having crop yields increasing throughout the world. However, the cost comes in the form of massive climate change.
Land emissions will be priced, but in contrast to SSP1, this is assumed to be delayed. We do not come up with a carbon price until 2040 in this scenario, which means that land-use topics are not addressed through pricing mechanisms until quite late in the century.
The Importance of Scenarios in International Negotiations
Real-World Application of SSPs
These SSPs are not just academic exercises or theoretical frameworks. These are the narratives that are currently being debated intensively in international negotiations around the pros and cons to specific countries of having these different scenarios happen. If you remember, the course had the lead scientist of NetCAP, Famara Adamfa, come in as a guest lecturer. He is a cool guy for a lot of reasons, but one of which is that he is a climate negotiator for his home country, the Gambia. These different SSPs are what countries debate over in international climate negotiations. If this scenario happens, what policies are going to happen in response, and how would that affect our country specifically? The science has focused on these scenarios, and so too have international negotiations and policymakers around the world.
To emphasize the importance of understanding these scenarios and their implications, the instructor wants to have a little in-class discussion where students engage in role-playing that emulates actual international negotiations.
In-Class Group Discussion Activity
Activity Setup and Instructions
The instructor wants to split the class into three groups of three to five people. Everybody in the back row is asked to relocate to one location to consolidate into discussion groups. The instructor would like the students to take on the role of their assigned country and talk about specific questions. The first question is: Which SSP do you think would yield the lowest well-being and welfare for the median human in 2100? Further, which SSP would be best and worst specifically for your country?
If students need a reminder of their country assignments, the instructor provides a list. If anyone has not submitted their country yet, they can just pick one for now and the final countries will be finalized later. The class will relocate into discussion groups and emulate the actual negotiations of countries. For now, they are just going to address this question about which SSPs would be best and worst overall, but the instructor asks that they spend more time specifically on their country. The instructor would like to have one person identified in each discussion group to report back to the full class discussion, indicating which countries and which scenarios were identified as best and worst. The back row group is going to have to consolidate since there are fewer people there, which the instructor acknowledges is no fun. Then the other groups will be the main discussion groups. The groups are asked to relocate and take about seven to ten minutes for their discussions.
Group Discussion Results and Analysis
After the group discussions conclude, the instructor asks the class to come back together. The instructor notes that they heard a number of answers on the first question about what is worst for well-being and welfare. The instructor suggests that maybe the first question was too easy because they framed it in a certain way. The instructor reasons that if there is one scenario that has low challenges, does not that sound like a good one? They ask what everybody thought about it.
The first group to report back says that they actually said that SSP1 was the best overall. But then for each of their specific countries, they identified SSP4 as the worst, because their countries all tend to have varying levels of lower incomes or income inequality. So SSP4 might actually have more direct negative impact on a lot of these regions than SSP3, even though SSP3 might be worst overall.
The instructor then asks the back group what they identified as the worst scenario for the overall median human in 2100, and how their different countries evaluated the scenarios. That group responds that SSP4 is kind of a middle ground where you are implementing some development of renewable resources for energy, but then you are still relying on a lot of the stuff that is destroying the ecosystems and rainforests. So it is kind of like a middle ground where you cannot really figure out what we are doing. For their countries, they considered Venezuela, Costa Rica, Brazil, and Dominican Republic. The instructor asks if there was any deviation on which countries thought which scenario was best and worst.
One student chimes in with an interesting observation. They note that if we go down SSP1 towards a green, sustainable planet, there is not going to be a lot of use for all of their country’s oil reserves. So they are kind of hoping we are going for SSP5, the maximum possible scenario, but still with the fairness of benefiting from that income. This illustrates the real tension in international climate negotiations between economic interests and environmental concerns.
The instructor notes that this is the first year they have done the exercise with a regional focus. They decided to do it in South and Central America, and also Portugal. The instructor admits that was a mistake in the grouping, but notes that the interesting thing is there is a little bit of similarity among many of these countries. They tend to be lower income, so there is more agreement on which would be the worst. The instructor mentions that previous years where they have done this with countries like Russia or China, you start to get broader deviation of how those interests play out. Russia, for instance, has relatively low damages from climate change happening, but pretty high damages from climate change being mitigated. So it results in some interesting outcomes where countries with different characteristics show very different preferences. This year, because the class has more of a regional negotiating block, which the instructor thinks is a little interesting, the dynamics are somewhat different.
Transitioning to Land Use Change
The Centrality of Land
The instructor says okay, well, thank you for that group exercise. We are going to keep on returning to these countries throughout the semester as we develop case studies. But they want to jump now to the other slide deck that we have on land use change.
Okay, so we have sort of had a progression through the material. We talked about climate change initially. Then when we talked about the SSPs, we talked about different drivers like population and policy. But throughout, the instructor kept coming back to this question of land. Land is really critical to understanding future sustainability.
Deforestation and other aspects of land use are going to be one of the determining factors in how the world feels in the future, for several reasons. Number one, land itself has a huge impact on climate change. Land-intensive sectors like farming contribute a little over a quarter of the total greenhouse gases out there. This is different, the instructor thinks, than what a lot of people think of when they consider climate change. We maybe think of cars and their tailpipes, or coal-fired power plants and their smokestacks as being the main things contributing to climate change. Those really matter, but so does agriculture, both in terms of cutting down forests to make more cropland, which emits carbon dioxide from all those trees that are no longer being stored, as well as direct inputs like tractors that burn fossil fuels.
How we manage our land for agriculture and other things, like grazing of livestock on pastureland or forestry, is going to be a big part of what sustainability looks like in the future. But the instructor wants to start and focus on how it is represented in applied economics. They are talking about data.
Satellite Data and Land Use Mapping
The Satellite Revolution in Data Availability
We probably all know that there are thousands of satellites overhead right now. This has resulted in a revolution in data availability. Data used to be something you collected by asking people, going door-to-door, like Census Bureau work. But satellites have made this global and increasingly precise. The number of different datasets you can derive from satellites is huge and constantly growing.
There are many different types of satellite-derived datasets available to researchers. Land cover and land use information is one critical dataset. Vegetation indices, which measure things like how healthy a plant is, are another important source. Elevation data can be obtained from space. Climate data, soil moisture information, and even specific crop types can be identified through satellite imagery. Looking at nighttime lights is relevant for detecting poverty from space. One dataset that the instructor really likes is that we can actually detect gravity from space using specialized satellites. This is useful because we can observe with a satellite how much water is in a reservoir underground, because water has a different gravity signature than rock. If we can detect gravity from space, we can measure the amount of groundwater available, which is incredibly useful for water resource management. It is surprising how many things we can measure by essentially sending satellites spinning around the earth and taking pictures or detecting various other phenomena.
That is really fun to think about. However, the key type of dataset that we are going to be using in this class is a land use land cover map. There is an example from the U.S. Geological Survey, one of many possible viewers, that the instructor is showing to the class.
Exploring the Twin Cities Land Use Map
This particular example shows the Twin Cities area. You can see the river structure in the map. We are right here in the metropolitan area. The instructor can tell our exact location because those are the crop fields that we have on the St. Paul campus for researching different animal and plant science technology. If you zoom in on this map, you can see these agricultural plots in the middle of the city, which is kind of surprising. The instructor actually uses this feature as a test to see how good different land cover databases are, by checking whether or not they correctly identify cropland in this location. It is right in the middle of the city, so it is kind of shocking that operational cropland exists there. But there are tons of different satellite-derived land cover datasets available that vary in their accuracy and coverage.
Getting Started with QGIS
Preparing Data for Analysis
The instructor has gone ahead and prepared a number of datasets for the class to use in analyzing land use change. The instructor wants to spend the last five minutes of class getting everyone up to speed on how to use the software, and then on Friday, when the class meets in the new room, we will spend more time doing this together in a much more interactive context where it will be much easier for the instructor to come around to different students and help them troubleshoot.
This work will actually begin right after we finish the micro quiz, so students should not forget about that assessment. The instructor will send an announcement reminding everyone. But what they would like everyone to do now is launch QGIS, and they just want to get a basic understanding of what is going on with the software.
Accessing the Data
One of the challenges is the data itself and where to get it. In the slides, the instructor has linked to two different places. Number one is the data folder. If you click on that, it will take you to a Google Drive folder. Right now, there is only one country, and the instructor chose Nicaragua because nobody in the class selected that country. Eventually, the instructor is going to populate this folder with data that they have made for all of the different countries that students have selected. But what they want to show everyone is just enough to get started playing with QGIS and using real data.
Once you go into the folder, there are going to be all these different layers of data available and the instructor wants everyone to focus on the LULC, which stands for Land Use Land Cover. You will see a few different files when you open that directory.
File Organization Best Practices
The first thing to note is that this data is obviously on Google Drive on the internet, so students are going to need to download this to their local computer. If you just double-click it from Google Drive, it is going to give you a pretty useless preview of the data. But the problem is that this is not a picture of a cat that you can just look at in a preview. This is data. We do not want to look at it like a picture or image. We want to get the raw data and import it into appropriate software. So make sure you download it to your computer.
The instructor would suggest starting early with the process of being specific about where you download things. The instructor recommends putting everything in a class directory that is well-organized. For the instructor, that is their username and then they organize things in folders. For them, it is a teaching folder, but for students, maybe it is a classes folder, then a subfolder for APEC 3611. It is tempting to download files just to your downloads directory, but the instructor cannot tell the class how much time they have wasted with people who have literally two thousand files in their downloads directory because that is the only place they have ever put anything. That gets quite challenging to manage and causes all sorts of problems. So the instructor strongly recommends that you organize files and put them in a class folder with a sensible naming structure.
Loading Data into QGIS
What the instructor wants to show everyone is how to load this data up in QGIS. The instructor has already gone ahead and downloaded it and opened up a file explorer. This example is on Windows, but you can use the Finder on Mac if you are using a Mac computer. But here is where the instructor downloaded the file. It is a pretty small file. Some of the other countries will have much larger datasets, so students should give themselves plenty of Wi-Fi bandwidth time for downloading. But just have this folder up and ready.
The easiest way to add something into QGIS is to open QGIS and put it next to the folder, so you can see both the QGIS application and the file that you downloaded from the internet. Once you have that set up, it is as easy as just dragging the data file over into the layers panel on the left side of QGIS. You can actually drag it pretty much anywhere in the interface. But when you drop it in the layers panel, we are telling QGIS to load up this data and have a visual representation of it on the map. Once it is loaded, you can zoom in, pan around, and do all the things you would like to do with geographic data.
Styling the Land Use Data
After loading the data, we want to make it a little bit more visually appealing and easier to interpret. The instructor has got some explanations in the slides, but if you are not following along, you can always refer back to those slides. To style the data, go ahead and select this layer and double-click on it. You are going to get a massive set of options and configuration menus. The instructor would like everyone to go ahead and click on the Symbology tab. The instructor thinks that might be default for some systems, but if not, just click on the one that says Symbology.
We are going to change one key option in the symbology settings. Instead of the render type being single band gray, which shows the data in black and white, the instructor wants you to change that to single-band pseudocolor. All that is going to do is say instead of black and white, we are going to take the data in this file and map it to different colors at different points on a color ramp. Once you have that changed, you can click OK, and now you get a slightly more pleasant-looking map in terms of appearance. More importantly, now you can actually tell the different classes in the land use data apart because they are rendered in different colors.
Wrapping Up the Class
Alright, now that the instructor has stalled a few minutes by explaining this process, the instructor asks if anybody did not get the data loaded up on their machine. The response is that most people got it loaded. The instructor says okay, and now we are going to go interactive here. This would be much easier in the new classroom with better computer setup. If the instructor had to guess, they would say that most of you have succeeded and are good to go. You can head out because we are at time. But the instructor would actually like to stop around and make sure everybody has it up and running and at least point everyone in the right direction for troubleshooting.
For those of you ready to head out, the instructor says have a good rest of your day. The instructor is going to troubleshoot these remaining problems really quick, and will see everyone again in room 135B, Rattan Hall, next time.
Software Recommendations and Technical Support
QGIS vs. Other GIS Software
One student asks whether they should print QGIS from the official GIS website and mentions that they have another option. The instructor clarifies that yes, you should get QGIS from the official QGIS website, and it is pretty different from other GIS software. The instructor wants to clarify why QGIS is being used in this class. QGIS number one is free and open source, which is important for a class where everyone needs access. Scientists tend to use QGIS because of its flexibility and power. If you were a city planner working for a municipality, you would probably use ArcGIS, which is the industry standard in professional planning. But the instructor thinks QGIS is by far the best open-source option available. It will be a little bit of a learning curve for those who have not used geographic information systems before, but students will still be way ahead of the game by learning QGIS.
Troubleshooting and Getting Help
The instructor says okay, so just open QGIS when you get home or some time before next class. One student confirms that they can just drag that file from the folder into QGIS, and the instructor confirms that yes, that is exactly right. All you do is drag it over from the file explorer into the QGIS interface. Yes, and there you go. You have got it loaded.
And so if there is anybody else who needs help, there are a few people still troubleshooting here at the end of class. The instructor will spend some time walking around and helping those students. The instructor will send out a reminder email with more detailed instructions before Friday class so that everyone has clear written instructions for downloading the data and loading it into QGIS at home before the more intensive computer lab session on Friday in the new classroom.
Transcript (Day 1)
Alright, let’s get started! Welcome to Chapter 17, Lecture 26. We’ve kind of gotten off that numbering system, and today we’re going to talk about future scenarios and the SSPs, including what that stands for.
The AI art for this lecture is particularly good. I plug in my title and ask it to make an image for this class’s lecture content. ChatGPT 5.4 is getting to know me and my class, and it knows what I’m trying to teach. The image shows a transition from an Armageddon-like world with smokestacks, planes, and scariness, to a beautiful vision of the future where supply and demand change over time. I hope we get there too. It misses some of the nuance, but that left side is quite scary.
Here’s what we’re going to do today: first, we’ll talk about country assignments related to the weekly questions you turned in last week. I’ve made assignments. Then we’ll move on to IPBES and what that acronym means. These frameworks are useful because they present very detailed scenario analysis and categorize scenarios into four different types. They also provide a full framework called the Shared Socioeconomic Pathways, or SSPs.
In totally unrelated news, I’ve been complaining about my computer being slow and freezing. PowerPoint has crashed, and I’ve wasted a collective amount of time. So I finally did it—I got myself a new computer. My rule with my wife is that I can only get a new computer if a student beats my computer in an in-class exercise. We haven’t done it yet in this class, but in my other class, students were beating me while running ecosystem service models. At first, I felt bashful and shameful because I see myself as a computer guy. But I was also excited because it meant I got to buy a new computer. I’m making a huge leap from Windows to Mac because Apple chips have gotten better. They used to be more energy efficient and user-friendly, and now they’re both more efficient and faster. These are the latest benchmarks on the Apple M5 chip—literally the best one I could get. I’m so excited.
More relevant to you is the country assignments. I did my best to assign countries based on your preferences. I had everybody list four preferences, which was probably overkill to minimize non-first-place rankings. If you didn’t get your first choice, I’ll allow you to send a petition email to make your case. If you’re Gabriella, Alex, or Kayla, feel free to email me. If I don’t hear from you by the end of today, I’ll assign you one randomly. Just let me know your preference, looking at the slide so you don’t claim one already taken.
I used an algorithm to minimize off-by-ones. When I went through it the first time, I did poorly and got four people, but I realized if I swapped things around, I couldn’t do better.
Reminder: tonight Assignment 3 is due. This corresponds with the micro quiz on Friday. After midnight—realistically sometime Tuesday morning—I’ll put up the answer key so you can see the correct mathematics behind it.
Now, let me talk about IPBES. It’s really a transition, but what is IPBES, and why do I care so much about it? I’m not going to write out the full name because I don’t feel like it. It’s the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Clearly, they’re not very good at names. Essentially, IPBES is a big convening body that pulls together the current state of science. The easiest way to explain it is that IPBES is the IPCC for nature.
Who’s heard of the IPCC before? It’s the Intergovernmental Panel on Climate Change. This is a much older body, established in the 1980s. It won the Nobel Peace Prize, shared with Al Gore, which I thought was funny. The IPCC is an organization of different nation representatives trying to synthesize everything there is to know about climate change and define the scientific consensus. Despite lots of debate, it’s useful to have an authoritative body represented by the best scientists and country delegates negotiating what exactly this consensus science is.
IPBES is just like the IPCC but instead of summarizing science on climate change alone, they look more broadly at biodiversity and ecosystem services. It’s a big organization hosted by the United Nations with 141 country delegates. The U.S. is no longer a delegate. About a month ago, the current administration formally removed the United States from IPBES, but we were part of it and probably will be again in a few years.
IPBES synthesizes existing scientific knowledge about biodiversity and ecosystem services into major assessment reports. They report on specific topics like sustainable use of wild species, values—how we value nature—and business and biodiversity. Steve Pulaski, co-founder of my NatCap research center team, is the chair of writing the business and biodiversity report. He’s incredibly busy. They put out a press release and got about a thousand media hits immediately. These are major reports that many people care about.
But in addition to putting out reports and defining consensus on biodiversity and ecosystem services, IPBES does a really good job tying together different frameworks for understanding how humans and nature relate. This is the IPBES conceptual framework that was published. We don’t need to go into all the details, except for one key thing.
IPBES is a negotiating body, meaning every report they produce needs buy-in through a consensus system from all 130-plus member states, including Indigenous communities and non-Western knowledge systems. This has been really hard. There are a variety of different ways of describing nature and how it relates to us. We have the word “nature.” We have “biodiversity and ecosystems”—these are science terms. But we also have “Mother Earth,” “systems of light,” or “intrinsic value.” The different member states debated what the relationship between humans and nature really is. Very Western scientific views see it as ecosystem services—there are production functions and ways it provides value to us. But others, like Ecuador, argued that’s not the right framing. We are a part of nature in a gift-giving relationship, very different from the idea of ecosystem services that we’re extracting from it. It was a really fascinating debate.
We’ll see at least three different phrases for approximately the same thing. “Ecosystem goods and services” is the one I emphasize throughout, given my Western scientific training. A middle ground is “nature’s contributions to people,” which flips the agency. We’re not extracting from nature—nature is contributing to us. Essentially, it points to the same set of things as ecosystem services. Then finally, “nature’s gifts” moves further away from that Western scientific, potentially narrow vision of the relationship.
As a side note, in my undergrad, I was a philosophy major for a long time, and I was fascinated by the question: what is the way humans should live with nature? At a very deep level, I was an environmentalist, but at first, it wasn’t about economics. I wondered if we should all live in natural huts or if there’s something fundamentally wrong about living surrounded by air conditioners and concrete. I thought long and hard about environmental ethics. It’s a fascinating topic.
Obviously, I changed direction. Environmental ethics didn’t seem like a very persuasive toolkit when talking with people who disagreed with me. I didn’t really like economics at the time—I saw it as the problem, the system I wanted to take down. But it turns out economics is very persuasive, so that’s exactly why I emphasize it. That said, we will talk about ecosystem goods and services, but there is this much deeper question of what relationship we ought to have with nature, and it may be very different from what we have right now.
Their conceptual framework also describes many other spectra that matter when analyzing this relationship: thinking about global versus local scales, and how things change over time. They emphasized wanting to know how different drivers—natural drivers like solar variation and anthropogenic drivers like carbon emissions—relate to the provision of nature and the value we get from it. Critically, how is that affected by our human systems, institutions, and governments? This conceptual framework provides a way of thinking about all these things in one combined system.
One of the most specific things that came out of this framework was that creating different scenarios of future livelihood was an important step in understanding how to analyze humans’ connection to nature. So I want to dive into that.
We’re talking about four types of scenarios. The first is exploratory. Exploratory scenarios are on the left part of this plot. You’ll notice a wheel going around the outside, showing different parts of the policy-making process. When talking about policy, the beginning is often agenda setting—you don’t know exactly what goals you want, so a good way to start is to explore the possible space.
For each type, I’ll draw a mini graph with the value of nature—ecosystem services or nature’s contributions to people—on the vertical axis. It’s the amount of value humans get from nature. There are three points: the past, the present, and the future. This is a fundamental way of describing time.
For exploratory scenarios, we have an observed era up to a decision point, then something we don’t know yet because it hasn’t happened. Exploratory scenarios look at different possible outcomes without saying what’s driving them. What happens if this type of climate change occurs? What if human behavior causing deforestation continues? Let’s explore all possibilities. This is open-ended, exploring what are the different targets we want—what state we hope to find ourselves in 2050 or 2100. It’s complex, and people disagree. If we can’t agree on the definition of the nature-human connection, people will definitely disagree on which scenarios we want. But this is where the agenda is set.
The second type is target-seeking scenarios, related in the process. Step one sets the agenda. Hopefully, you agree on what outcomes you want. Target-seeking scenarios have a simplified graph. The first part is the same—we have the same observations showing nature’s value going down. What’s different is we have a goal, a star on the horizon (actually it’s a diamond). If we set that as our goal, we explore designing different routes to get there. Target-seeking involves policy design. We’ve got a target we’re seeking—what’s the best way to get there?
This is hopeful because we know where we want to go and assume we can design policies to hit that target. But the third type is more realistic in terms of how governments actually proceed: policy screening. Policy screening shares the idea that we have a goal we want to hit. But we don’t get to play God and make the world exactly how we want it. These lines don’t necessarily lead right to the target. Policy screening focuses more on implementation. We have imperfect policies where one may overshoot, one may undershoot, and it’s hard to figure out if it even succeeds. This is closer to how governments actually think about implementation. Maybe one policy is setting a carbon tax, and another is setting caps on carbon emissions from coal-fired power plants. This is talking about actual laws that we might write and whether they succeed at getting to our objective.
I’ll share a couple of stories about this because scenarios have been massively important throughout my life. The first is from my PhD. I got a postdoc where I was supposed to study how different countries incorporate ecosystem services into their sustainable development goals. I was specifically charged with being the scenarios guy. I had to give a presentation at a professional level to a group of PhDs on what scenarios are. I hadn’t seen all the different approaches to scenarios in the literature, and I only thought of it in the policy screening space. I’d written one paper on it and gave an awful presentation basically saying here’s one map of what the world looks like if we did Policy A, and here’s another map if we did Policy B. Scenarios are just defining these maps accurately. What’s the big deal?
Well, what I learned later is that 90 percent of that group were on the agenda-setting side. They were social scientists who didn’t like numbers. They liked narratives. They spent their days talking with local residents, farmers, different community leaders, trying to elicit narrative descriptions of what future world they want. Instead of maps and numbers, they were embedded in their communities, thinking about justice, value, and social systems. They were appalled that I was over-quantifying it and missing the key point: scenarios are really about meeting with stakeholders, asking what they want, and describing their preferences. Maybe eventually it gets into a map, but this group saw my results very differently.
The second story is the complete opposite. I had written a paper that moved into the policy screening space where I tried to find the optimal policy for achieving the desired world. Instead of two different maps, I had a single map showing how we want the world to be—this is where agriculture should be, this is where forestry should be, and we looked at the most cost-effective way of getting there. We presented this work, and the World Bank wanted to fund us because they liked what we did. But I talked to them and said, look, these are the different policies that could get there. They looked at me and said, “Do you think you can play God?” That’s where that phrase comes from. You can’t just set what the landscape is. That’s not a policy you can write in a way a lawyer could read. They were interested in policy screening. They wanted something that could be written as a law, a policy, or a loan decision. They’re a bank, and what they do is make loans to different countries building big projects with environmental impacts, like dams. They wanted to know ways they could change their policy in language that takes into account the value of nature.
I’ve had a lot of learning. I guess my next one will be the fourth type: retrospective or policy evaluation. Retrospective is exactly what it sounds like. We focus on not the future part but the past. We observe a period where we did implement a policy and estimate the implementation gap or how much we missed out on because we didn’t take action. This establishes how well we did. Was the cost-benefit analysis we originally calculated actually right? A lot of current economics focuses on this, doing statistical analysis on whether a policy had a causal impact.
But it’s a cycle because ultimately, when you do retrospective analysis, you learn something about implementation that feeds back into the next phase of decision-making. This new data helps you better assess what options you might want. It’s an iterative process.
I find this useful outside the environment too. If you’re a business analyst, it’s the same process. You do market research—that’s agenda setting. What products might we produce? Then, okay, we want to be in this market. What’s the best way? What manufacturing policies might we employ? Then, did we do a good job? Being able to discuss this framework with a CEO or someone you’re debating with is a really effective way of honing arguments because people talk about different scenarios, but they’re talking about different phases of this cycle. Knowing where you are really helps.
Any questions about the scenarios framework? We’ve been talking about scenarios already. I didn’t want to present this at the beginning because we wouldn’t have had enough information to make it make sense. But it’s what underlies the Shared Socioeconomic Pathways.
SSPs were coupled with the IPCC climate science to find a specific set of scenarios. The SSPs were led by a huge multi-team, multi-stakeholder engagement effort combining all parts of the scenario process into five specific pathways that are hopefully useful. The goal was to do the same thing the IPCC did. The IPCC is known for representative concentration pathways—what we talked about in the last few lectures. They wanted a complement to the RCPs, but broader than just climate variables. It covers all the different socioeconomic pathways.
Instead of concentration pathways measured by CO2 concentration, we’re measuring socioeconomic status. That’s a big difference. The percentage of CO2 molecules is pretty simple and easy to understand. Socioeconomics is much more complicated. This means combining all parts of the scenario process into a specific database. It starts with narratives from social scientists who elicit different storylines about what people care about. Then there’s work to say, alright, let’s take a narrative and think about what it means for specific variables like population, urbanization, or GDP. If a scenario involves people reconnecting with nature, maybe that corresponds to reduced urbanization. This maps storylines to narratives and also maps storylines to policy scenarios.
This generates two different things for useful information in a database. The assumptions give us databases of population, urbanization, and GDP under different storylines. We also put all this information through different models—integrated assessment models—to generate predictions of future outcomes. So the assumptions are one thing, but we also get predictions about GDP growth, poverty, food security. All these are saved in a giant database coupling everything together, giving us useful scenarios to explore policies we care about.
But I want to be more specific. There are five scenarios—there could be infinite, but these five are defined with rigor, necessary if you want a whole database. We’ll organize them using this figure. First, all scenarios are defined by a number: SSP1, 2, 3, 4, or 5. Each has a narrative underneath it, but what I want to get to first is how to interpret the axes of this graph.
The vertical axis comes from the IPCC and shows challenges for mitigation. Mitigation refers to preventing CO2 emissions. Our scenarios are defined by high challenges for mitigating or low challenges. Low challenges might come if renewables are really cheap or we successfully negotiate international policy, though the latter doesn’t seem likely. The renewable scenario does seem more likely. High challenges might come if we can’t figure out how to replace fossil fuels. Aviation fuel has always been listed as one of those hard-to-replace things. If we can’t, we’ll find ourselves in these higher areas where mitigation challenges dominate.
The horizontal axis is adaptation challenges, related to climate change but instead of mitigating by making less climate change happen, adaptation means dealing with climate change. On the right, we have more adaptation challenges. Low challenges might come if we invent really good cheap technology like fusion or invest in quality health infrastructure. Yes, we have more heat waves, but we have cooling centers for people to find respite. A key factor is what happens to human capital related to education and income inequality. In really unequal societies, some people can protect themselves while homeless people can’t do much in a scorching concrete city. High challenges mean unprotected poor people. I think of adaptation as essentially air conditioners. I love air conditioners, and I spend time with environmentalist friends who hate them. They say it’s so unnatural, how can you like this? Aren’t you a real environmentalist? I say I’m not, really. I think of it as a utility-producing box. Plug money and electricity in, get happiness out. That’s what adaptation is—using money to mitigate costs, and air conditioners might literally be the savior of humankind in many locations.
With these two axes, we have five scenarios. Starting in the middle, SSP2 is the middle of the road with moderate mitigation and adaptation challenges. It’s often what we call Business As Usual.
SSP5 is up here with mitigation challenges dominating but we’re pretty good at adaptation. SSP3 is the bad one with high challenges for both axes. SSP4 is down here. Then there’s the good one, SSP1, with low challenges for both.
These scenarios are coupled with data, plugged into models, but what does it actually look like? Someone has to walk those narratives into specific variables. Here’s a tiny subset: SSP1 will have relatively small cropland expansion and relatively low population. Key variables include livestock in the diet, how many trillion kilocalories from beef, GDP, fertilizer usage, irrigation—things that make or break whether scenarios can happen. We define all these for different scenarios.
Each SSP, in principle, could be applied to any amount of climate change that actually happened. In international negotiations, we see SSPs coupled with RCPs. SSP1 is usually paired with RCP 2.6. In principle, you could couple any SSP with any RCP, giving you many possible frameworks with all scenarios mixed and matched with different RCPs.
But because a sustainable world probably also has successful climate mitigation, common pairings are most used. You often see SSP1-RCP 2.6, like that notation. We call these marker scenarios. The notation often compresses it, so you’ll see SSP126 or SSP5-RCP8.5.
For each SSP, we get plots similar to climate change plots showing temperature diverging into the future for different RCPs. Now we’re showing socioeconomic information for different SSPs: population indicators, education, literacy share, urbanization, GDP, income inequality or Gini coefficient, all plotted forward. We also see specific technology details. This triangle shows different energy solutions. The bottom vertex is renewables and nuclear, one side is coal, another side is oil and gas. The blue line shows our progress through time starting around 1858.
We were a renewable society back then because we didn’t know how to burn coal and got power from human muscles, animals, water wheels, and other renewable sources. Around 1900, the Industrial Revolution kicked into full steam. We knew how to make steam and it became widely implemented. We rushed toward coal, then eventually oil and gas. By 2010, we have a mixture of oil-gas, coal, and renewables.
Different SSPs chart different pathways going forward. SSP3 has lots of coal. SSP1 and SSP4 have lots more renewables. Where we go in this space captures the international energy policy debate. There are pros and cons to each pathway, but SSPs define it very specifically. You can see predictions of how much oil, gas, nuclear, biomass, and renewables exist under different scenarios. Then we introduce a bunch of different models.
This is a lot of information, I know. We’re thinking about environmentalism at the global sustainability scale and trying to pull all these systems together, which is hard. We didn’t get to the narratives part. We’ll pick this up next class. I have the official narratives and my summarizations. We’ll have an in-class exercise about those. But before we break, here are two more mechanical things.
Number one: the specific reason I like SSPs is because they’re consistent, quantitative, and have a database. It’s a super useful database. I’ve put a link here, which may be super useful when you do your country reports. You can get access to all these predictions for the different scenarios in downloadable format useful for reporting your country’s sustainability prospects.
Number two: I want you to install QGIS. I’ll send a link via announcements after class, but it’s QGIS.org, not too hard. This is a tool we’ll use multiple times throughout the rest of the semester for geographic information systems. It’s free, awesome, and works on Windows, Mac, and Linux. When you download it, get the latest release. We’ll all use the 4.0 release. It’s a big file, which is why it’s homework—get started in advance, not right before class, so we don’t all try to download it and swamp the Wi-Fi. Download it and make sure you can open it, because we’ll dive right into it next class.
Any questions? We’re getting more hands-on. It’s going to be fun. I really like spatial data, so that’ll be evident. Have a good rest of your Monday.
Transcript (Day 2)
Alrighty. Well, let’s get started. Welcome to day two, talking about scenarios. We’re going to finish the SSPs up pretty quickly. We’re going to talk about the narratives—that’s the component we didn’t yet talk about. But first, a few logistical notes.
Next class, we are going to be located in a different classroom. We’re going to be in Rattan Hall, so the same building, but 135B. I’ll send out an announcement, and if you come here next class and nobody’s here, you’ll probably remind yourself, oh yeah, we’re elsewhere. We’re going there just because it has much better seating for the interactive, computer work we’re going to be doing a lot more of for the rest of the semester.
We’ll actually do a little bit of it today, so it’s unfortunate that we’re not already in that room. But I’ll be filtering around, looking at people’s screens and helping get us all onto the right pages. I’ve reserved it for the rest of the semester, so I think it’ll be every class. But if we don’t like it, we can come back here. I’ll be taking opinions. I like it better.
Okay, so we’ll pick up where we left off on the SSPs and discuss those narratives. We’ll finally get to that group exercise that will be specific to your country. And then we’ll switch over to the next slides that are already posted online on land use change. And if we have time, we will pivot into using the software that I had you install over the last few days: QGIS. First, a show of hands. Who got that up and running successfully on their computer?
Excellent, that’s pretty good compliance. It was a really slow download, and the reason is because they just released a brand new version that had been long awaited—the 4.0 release. Their servers were swamped with lots of people downloading it, so it was much slower than normal.
So let’s dive right in. I want to pick up where we left off.
I’ll quickly remind us of the key plot that we’ve been using to organize our SSP scenarios, but then dive into the actual narratives that inform each of those. So just to get it back on the board, it’s going to be defined by our two axes: Adaptation Challenges and Mitigation Challenges. Again, those are two key words. Mitigation is keeping CO2 and other things out of the atmosphere to mitigate climate change from happening. Adaptation is acknowledging that it’s going to happen and figuring out how we can adapt.
We had our middle-of-the-road SSP2, the good SSP1, the worst SSP3, and then SSP5 and SSP4. That’s just to remind us. This is referred to as the challenge space by one of the earliest articles put out by IPBES and the IPCC.
I’ve already made the point that these scenarios are defined by really detailed data on projections of GDP, population, land use, and such. But what’s under the hood? These are the quote-unquote official narratives. This is a short paragraph put together by an expert community to define what this scenario is supposed to illustrate.
We’re going to start by giving them the different names that they go by, in short. SSP1 is the Green Road scenario, characterized by this sentence and these key components: land use is strongly regulated. This is one of the key things that, in addition to climate change, really matters—how are we going to use our land? It denotes that tropical deforestation rates are going to be greatly reduced.
Simultaneous to this, it assumes that crop yields are rapidly increasing, especially in low- and medium-income countries. Who can think of a reason why these two statements are tightly related? Why would the statement about deforestation going down have anything to do with crop yields going up?
Exactly. What do you not have to do if your yields are higher? Clear the land, right? A whole lot of work is going into research and development to see how we can increase our yields, on the presumption that then we won’t have as much deforestation. This is an appealing option because it’s sort of a win-win. Farmers like it when their yields go up. They really don’t like being told not to farm their farmland or that they can’t convert a patch of forest into farmland. So it’s a win-win where crop yields go up and there’s a positive environmental outcome.
A related point is that healthy diets in this SSP are assumed, with much lower animal calories as a share of all nutrition that goes into the average person. This is also related to land use. Who can see why changing towards lower meat diets would result in less cropland expansion?
Yeah, exactly. If we’re getting our calories from beef, we have to think not just about the land that the cow was on—unless they’re fully grass-fed, which most aren’t—but also the land necessary to grow the grain. For cows, it’s between 8 and 10 times more land necessary to grow the grain than you would need to get the same amount of calories if you just ate the grain yourself. Our digestive system is not very efficient, so if we want to make calories, we’re going to need a lot more land if those calories come in the form of meat than if we had just eaten the underlying grain ourselves.
Land use emissions are assumed in this scenario to be priced at the level of carbon prices in the energy sector. That’s a long way of saying they have a carbon tax, and it’s not just on the easy things like coal-fired power plants, but it also applies to land-based emissions. These are the key things that make SSP1 good. Mitigation comes out here because we have less land being converted from carbon-rich sources to cropland. The mitigation will be relatively low in the challenges, but also adaptation is relatively low because we’re effective at increasing yields. So both things are good. There are many more variables that go into this definition, but those are the key ones we’re going to focus on.
Okay, so SSP2 then. That’s the middle of the road—just another way of saying business as usual. Development and income continue to proceed the way they are, which is really unequal. Specific to these key variables, we’re going to have a less optimistic world where crop yields increase, but the rate of increase is declining over time.
This is kind of a scary situation. We’re in a world where we’ve been experiencing massive, unprecedented increases in crop yields, and this makes a lot of the system work. But there’s strong and growing evidence that the rate of yield increases seems to be plateauing. This is a problem, because a lot of our projections for future food security depend on yields continuing to go ever up and up. Actually, University of Minnesota is sort of famous for some of the research on this. Way back in 2013, we had one of the first papers to really look at global statistics at a gridded level on where yields were slowing their rate of improvements.
This scenario assumes instead of a shift towards plant-based diets, caloric consumption and animal calories converge towards high levels. China, for instance, which has traditionally consumed less meat per capita, basically catches up to the level of meat consumption in the rest of the world. Same thing with India and other developing countries as they converge towards the diet of high-income countries.
The result is that deforestation is not really incentivized until later on. There’s a time element to this. Not until 2030 in this middle-of-the-road scenario do we start to have effective policies for preventing deforestation.
That’s the most boring one. It’s what would happen if we just keep doing what we’re doing. But the space also allows us to look at a variety of other ones. Let’s go to the worst one: SSP3, Rocky Road—which I love as ice cream, but not so much as a road to drive on.
This one is characterized by several things, but the key one is these first few words: resurgent nationalism. When this came out, quite a while ago in 2012, a lot of people disregarded this one, wondering whether it’s all that important to consider scenarios with resurgent nationalism. Many felt the world was inevitably heading towards more international negotiation and trade. But yeah, this one’s become more relevant. There’s been a marked increase in nationalistic policies pursued here in the United States and throughout the world.
Why is this important from a sustainability lens? Countries will be competing with each other instead of working towards a common goal of solving climate change. The idea of free-riding makes more sense. If you only care about your one country and think all the countries around you won’t cooperate, it increases the case that you should just protect your own interests and free ride on everybody else’s, rather than waste money solving a problem for other countries you hardly benefit from.
This is a challenging scenario, and it will have divergence in addressing ecological and economic inequality over time. In terms of land, land use will be hardly regulated. You can sort of do whatever you want. Rates of crop yield increase will decline over time, but especially badly in developing countries. This is a key difference from SSP2, where developing countries are closing the gap in many things. But what if we have a nationalistic, isolationist approach? There’ll be less trade. There might be less sharing of technology and investment from high-income countries into low-income countries that facilitates the uptake of technology. These scenarios will have very limited transfer of new agricultural technologies to developing countries.
This one will have unhealthy diets with high animal shares and high food waste. You know, we waste a little under 50 percent of our food. Some people say that’s one of the most effective levers we could pull to minimize the environmental impact of our food consumption. I’m really skeptical of that argument, because I can’t figure out any good policy to do it. What policy would encourage a buffet to have smaller amounts of food?
Raise the price on food would be one way, but my challenge would be that this is one of the few things where raising the price might have especially negative consequences. If we raise the price on luxury cars, no big deal. If we raise the price on food, that might have real knock-on impacts on affordability. But this is basically the argument we use for other things—putting a carbon tax on food based on how much carbon it contains or on waste.
Yeah, I could be persuaded, but it’s one of those that I think is harder to solve. I really care about the environment, and I waste food. I have a hard time not accidentally buying the wrong amount and having it go bad in my refrigerator. I don’t know if anybody else has that problem, but I have a stinky refrigerator at times.
A key aspect of SSP3 is that with regional rivalry and the focus on nationalism, there will be much less trade. A lot of environmentalists might cheer this option, thinking trade is bad for the environment. Well, in this world, it’s quite the opposite. If you have trade, you can allow high-efficiency agricultural-producing countries to trade their food to less efficient ones, and this reduces the total cropland needed to feed the world. If we didn’t have this trade on agricultural goods, each country would have to be self-sufficient and produce more locally, even if they’re not very good at producing all the different foods. We’re really good here at producing corn and wheat, not so good at bananas. If we focus on self-production, we’ll be achieving lower productivity outcomes.
So SSP3 has that rocky road character. SSP4 turns it up to eleven. There will be tons of inequality, but you won’t have the bad mitigation. This would be one where we have highly unequal investments in human capital, and this leads to some good things like greater emphasis on low-carbon energy sources. It solves part of the climate problem, but there’s very little development of technology in low-income countries.
We get a divergence where we’d have highly effective regulation of land use and deforestation in high-income countries, but in low-income countries that tend to be tropical, there will be relatively less prevention of agricultural expansion. What is it about low-income countries tending to have tropical ecosystems that would make this especially bad? What do we know about tropical areas? Yeah, the rainforests and their biodiversity. This is something we’ll be coming to much more throughout. But it’s an awful twist of reality that the countries with the lowest incomes also tend to have the richest endowment of biodiversity, of rainforests, and really important ecosystems.
So not having land expansion regulated in tropical, low-income countries is especially bad. If we have deforestation in Minnesota, that’s bad. We lose a few white-tailed deer or something. But if we have it in places that are rainforests, these places have ten times as many species of animals present, and many are endangered. So for a given hectare of deforestation, it’s much, much worse if it happens in low-income countries on average.
This divergence also comes out in crop yields. High-income countries continue to increase their yields, but low-income countries do not. There’s a convergence towards a medium level, and food trade remains globalized. But access to these markets will be limited in low-income countries. The ability to buy fertilizer in Sub-Saharan Africa is quite challenging. There aren’t sufficient markets that provide fertilizer, so it’s either very scarce or very expensive, and as a result, not used very much.
That’s Road Divided. And the last one is SSP5: Fossil Fuel Highway. This world has good things and bad things. It will be one that emphasizes fossil fuels but results in rich societies. So what if we continue our basic emphasis on competitive markets, rapid innovation, and technology advance, and basically burn a lot of fossil fuels to make ourselves as rich as possible?
For mitigation, it means we have a ton of challenges. We’re going to have a ton of climate change, and this is the SSP typically paired with RCP 8.5—really bad climate change. But what this also shows is that there are benefits, and that’s why being an environmentalist is sometimes quite complex. If we get really rich off fossil fuels, we could spend that on adaptation. Everybody could have good air conditioners, or we could have really good public health systems. That’s a big assumption—that we would get rich and spend it on those things. If we got rich and didn’t spend it on adaptation, we’d be over here, right? But it’s a possible future where we continue to have increased climate change but spend it on dealing with the damages, especially for those most at risk.
In terms of land, it’s incompletely regulated. Tropical deforestation continues, although it’s assumed to slowly be declining over time. But what do we get from our investment in burning our stock of fossil fuel capital? Crop yields going up. This would be where we put tons of fertilizer everywhere, successfully solving the missing markets for fertilizers in Sub-Saharan Africa, and having crop yields increasing throughout the world.
Land emissions will be priced, but in contrast to SSP1, this is assumed to be delayed. We don’t come up with a carbon price until 2040.
These are the SSPs. These are the narratives. The reason this is so important and why I’m spending so much time on this is that right now, international negotiations are debating the pros and cons to specific countries of having these different scenarios happen. If you remember, we had the lead scientist of NetCAP, Famara Adamfa, come in. He’s a cool guy for a lot of reasons, but one of which is he’s a climate negotiator for his home country, the Gambia. These different SSPs are what they debate over. If this happens, what policies are going to happen, and how would that affect our country?
The science has focused on these, and so too have international negotiations and policymakers.
To emphasize that, I want to have a little in-class discussion. We’re going to split into three groups of three to five people, and everybody in the back row, if you could relocate to one location. I’d like you to take on the role of your country and talk about these questions: Which SSP do you think would yield the lowest well-being and welfare for the median human in 2100? And further, which one would be best and worst for your country?
If you need a reminder, here are the countries. If you haven’t submitted yours, you can just pick one for now and we’ll finalize it later. We’re going to relocate into discussion groups and emulate the actual negotiations of countries. For now, we’re just going to address this question: Which ones would be best and worst overall, but spend more time specifically on your country?
I’d like to have one person identified in the group to report back to the full class discussion, indicating which countries and which scenarios were identified. So this group over there, sorry back row, you’re going to have to consolidate. I know that’s no fun. Then this group here will be the discussion groups. Go ahead and relocate and take about seven to ten minutes.
[Group discussion]
Alright, let’s come back together. I heard a number of answers on the first question. That was maybe too easy because I framed it in a certain way. If there’s one scenario that has low challenges, doesn’t that sound like a good one? What did everybody think? Let’s go to this group first. What was worst for well-being and welfare of the median human in 2100 across the world?
We actually said that SSP1 was the best. But then for each of our specific countries, we said SSP4, because they all tend to have varying levels of lower incomes or income inequality. So SSP4 might actually have more direct impact on a lot of these regions than SSP3.
Excellent. Let’s go over to the back group. What was the worst scenario for the overall median human in 2100? And how about for your different countries?
It’s kind of a middle ground where you’re implementing some development of renewable resources for energy, but then you’re still relying on a lot of the stuff that’s destroying the ecosystems and rainforests. So it’s kind of like a middle ground where we can’t really figure out what we’re doing.
For countries, we did Venezuela, Costa Rica, Brazil, and Dominican Republic. Was there any deviation on which countries thought which scenario was best and worst?
Yeah, that’s a good point. If we go down SSP1 towards a green, sustainable planet, there’s not going to be a lot of use for all my oil reserves. So I’m kind of hoping we’re going for SSP5—the maximum possible—but still with the fairness of benefiting from that income.
Yeah, excellent. This is the first year I’ve done it with a regional focus. We decided to do it in South and Central America, and also Portugal. That was a mistake, but the interesting thing is there’s a little bit of similarity among many of these countries. They tend to be lower income, so there’s more agreement on which would be the worst.
Previous years where I’ve done this with countries like Russia or China, you start to get broader deviation of how those interests play out. Russia, for instance, has relatively low damages from climate change happening, but pretty high damages from climate change being mitigated. So it results in some interesting outcomes. Here we have more of a regional negotiating block, which I think is a little interesting.
Okay, well, thank you for that. We’re going to keep on returning to these countries. But I want to jump now to the other slide deck that we have on land use change.
Okay, so we’ve sort of had a progression. We talked about climate change. Then when we talked about the SSPs, we talked about different drivers like population and policy. But throughout, I kept coming back to this question of land.
Land is really critical. Deforestation and other aspects are going to be one of the determining factors in how the world feels in the future, for several reasons. Number one, land itself has a huge impact on climate change. Land-intensive sectors like farming contribute a little over a quarter of the total greenhouse gases out there. This is different, I think, than what a lot of people think of. We maybe think of cars and their tailpipes, or coal-fired power plants and their smokestacks as being the main thing contributing to climate change. Those really matter, but so does agriculture, both in terms of cutting down forests to make more cropland—which emits carbon dioxide from all those trees that are no longer there—as well as direct inputs like tractors that burn fossil fuels.
How we manage our land for agriculture and other things—like grazing of livestock on pastureland or forestry—is going to be a big part of what sustainability looks like in the future. But I want to start and focus on how it’s represented in applied economics. We’re talking about data.
We probably all know there are thousands of satellites overhead. This has resulted in a revolution in data availability. Data used to be something you collected by asking people, going door-to-door, like Census Bureau work. But satellites have made this global and increasingly precise. The number of different datasets you can derive from satellites is huge.
Land cover and land use—we’ll talk about that. Vegetation indices—like how healthy a plant is. Elevation, climate, soil moisture—you can even get crop types. Looking at nighttime lights is relevant for detecting poverty from space. One that I really like is that we can actually detect gravity from space. This is useful because we can observe with a satellite how much water is in a reservoir underground, because water has a different gravity signature. If we can detect gravity from space, we can measure the amount of groundwater available. It’s surprising how many things we can measure by essentially sending satellites spinning around and taking pictures or detecting various other things.
That’s really fun. Here’s an example you can click on if you want to explore, but the key type of dataset we’re going to be using is a land use land cover map. This is from the U.S. Geological Survey, one of many possible viewers.
This is the Twin Cities. We can see our river structure here. We’re right here. I can tell that because those are the crop fields that we have on the St. Paul campus for researching different animal and plant science technology. You can zoom in on it. I actually use this as a test to see how good different land cover databases are—whether or not they identify cropland here. It’s right in the middle of the city, so it’s kind of surprising. But there are tons of different datasets.
I’ve gone ahead and prepared a number of them for us all to use. I want to spend the last five minutes of class getting us all up to speed, and then on Friday, we’re going to spend more time doing this together in our new room, where it’ll be much more interactive and easy for me to come around.
Right after we finish the micro quiz, so don’t forget about that. I’ll send an announcement reminding you. But what I’d like you to do now is launch QGIS, and I just want to get a basic understanding of what’s going on.
One of the challenges is the data. In the slides, I’ve linked to two different places. Number one, the data folder. If you click on that, it’ll take you to this Google Drive folder. Right now, I only have one country, and I chose Nicaragua because nobody chose that. Eventually, I’m going to populate this with data that I’ve made for all of your different countries. But what I want to show you is just enough to get started playing with QGIS.
Go in here. We’re going to use all these different layers for a bunch of things, but for now, go ahead and focus on the LULC—that’s Land Use Land Cover. You’ll see a few different files.
The first thing to note is this is obviously on Google Drive on the internet, so you’re going to need to download this. If you just double-click it, it’s going to give you a pretty useless preview. But the problem is this is not a picture of a cat—this is data. We don’t want to look at it like a picture. We want to get the raw data. So make sure you download it.
I would suggest starting early with the process of being specific about where you download things. I recommend putting everything in a class directory. For me, that’s my username and then I organize things in files. For me, it’s teaching, but for you, maybe it’s classes, then a folder for APEC 3611. It’s tempting to download files just to your downloads directory, but I can’t tell you how much time I have wasted with people who have literally 2,000 files in their downloads directory because that’s the only place they’ve ever put anything. That gets quite challenging. So organize it and put it in a class folder.
What I want to show you is how to load this up in QGIS. I’ve already gone ahead and downloaded it and opened up a file explorer. This is Windows—you can use the Finder on Mac. But here is where I downloaded it. It’s a pretty small file. Some of the other countries will be much larger, so give yourself plenty of Wi-Fi. But just have this folder up.
The easiest way to add something into QGIS is to open QGIS and put it next to the folder, so you can see both the QGIS application and the file that you downloaded from the internet. Once you have that, it’s as easy as just dragging it over into the layers panel. You can actually drag it pretty much anywhere. But when you drop it in, we’re telling QGIS to load up this data and have a representation of it. You can zoom in, pan around, do all the things you’d like. This is just our first foray into the basic tool.
Let me make it a little bit more pretty. I’ve got some explanations in the slides, but if you’re not following along, you can refer back to the slides. Go ahead and select this layer and double-click on it. You’re going to get a massive set of options. I’d like you to go ahead and click the Symbology tab. I think that might be default for some, but if not, click on this one here.
We’re going to change one key option. Instead of render type single band gray, I want you to change that to single-band pseudocolor. All that’s going to do is say instead of black and white, we’re going to take the data in this file and map it to different colors at different points on the color ramp. Once you have that, you can click OK, and now you get a slightly more pleasant-looking map, insofar as you can actually tell the different classes apart.
Alright, now that I’ve stalled a few minutes, did anybody not get it loaded up on their machine? Okay, we’re going to go interactive here. This would be much easier in our next classroom. If you did, you’ve succeeded and you’re good to go. You can head out because we’re at time. But I’d actually like to stop around and make sure everybody has it up and running and at least point you in the right direction.
For those of you ready to head out, have a good rest of your day. Let’s just troubleshoot these problems really quick, and I will see you again in room 135B, Rattan Hall, next time.
Yes, you should print QGIS from the official GIS website. It’s pretty different. QGIS number one is free and open source. Scientists tend to use it. If you’re a city planner, you’re probably going to use ArcGIS. But I think QGIS is by far the best open-source option. It’ll be a little bit of a learning curve, but you’ll still be way ahead.
Okay, so just open QGIS. Yeah, so I can just drag that file from the folder into QGIS. All you do is drag it over. Yes, and there you go. You’ve got it loaded.
And so if there’s anybody else who needs help, we have a few people still troubleshooting here. I’ll send out a reminder email with more detailed instructions before Friday.