Two decades into the twenty-first century, we find ourselves facing the existential challenge of creating a sustainable version of human civilization. The scale of human activities is pushing hard on the tightly linked planetary systems that make up Earth’s climate. As the planet begins to move off into a different climate state, our project of civilization will, at the very least, find itself under stress. At worst, Earth’s changes may make our project impossible to maintain.
We urgently need to adapt civilization so that it can continue for the long term, so that it can become fully and globally sustainable. But before we can start working toward that goal, there’s an equally urgent question that often goes unstated: How do we know that’s even possible? How do we know there is such a thing as a long-term version of our kind of civilization? Most discussions of the sustainability crisis focus on strategies for developing new forms of energy or the projected benefits of different socioeconomic policies. But because we’re stuck looking at what’s happening to us as a singular phenomenon—a one-time story—we don’t think to step back and ask this kind of broader question. To even in pose it seems defeatist. But it must be addressed if we are to make the most informed, intelligent bets on the future.
Let’s be clear about what our question implies. Maybe the universe just doesn’t do long-term, sustainable versions of civilizations like ours. Maybe it’s not something that’s ever worked out, even across all the planets orbiting all the stars throughout all of space and time. Maybe every technological civilization like ours has been just a flash in the pan, lighting up the cosmos with its brilliance for a few centuries, or even a few millennia, before fading back to darkness.
This question speaks directly to Fermi’s Paradox. Perhaps the bottleneck we face today explains the Great Silence of the stars. Our question points to the final factor in Drake’s equation—the average lifetime of civilizations. Even if every planet orbiting every star in the universe evolved a civilization, it would still be possible that none lasted very long. That kind of fate might be universal for exactly the same reasons we find our own future challenged.
So, does anyone make it past the challenge we now face?
Staring down that question is where the rubber really meets the road in the astrobiology of the Anthropocene. The pessimism line tells us that, unless the universe is highly biased against the appearance and evolution of civilizations, others came before us. Each of those civilizations will have had a trajectory of development in terms of their growth and their impacts on their planets. Those trajectories are what we want to understand. Given what we have learned about planets and climate, there are good reasons to argue that many planets evolving a young, energy-intensive civilization will be driven into an Anthropocene-like transition. If there have been exo-civilizations before us, we’ve already learned enough about “thinking like a planet” to see if the conditions leading to Anthropocenes are common or rare. So, how can we use the science we know, gained from the planets we have seen, to begin a science of the civilizations we haven’t?
HOW NOT TO DO A SCIENCE OF EXO-CIVILIZATIONS
Prosthetic foreheads. That’s what you want to avoid—the Klingons, the Vulcans, the UFO aliens with the big heads. Science fiction has given us enduring images of alien races. Not surprisingly, most of them look a lot like us, but with different kinds of foreheads or ears or a different number of fingers on their hands.
In developing our science of exo-civilizations, we’re not interested in what aliens might look like or how they might behave. We’re going to avoid the specifics of their biology and their sociology because science provides us little to work with on those issues.
So, what issues can science help us with? There are three terms from Drake’s equation that make up the biotechnical probability. They involve basic biology (the origin of life), evolutionary biology (the rise of intelligence), and sociology (the development of societies). When it comes to what might happen on other planets, we are on murky ground for each of these terms. But if we ask the right questions, there are principles that constrain our theoretical explorations. These constraints are like guide rails keeping our theoretical bowling balls from plunging into the gutter.
For the basics of life, for example, we are going to have to rely on our knowledge of chemistry. But we already know that chemistry works the same way in distant regions of the cosmos as it does on Earth. From observations of interstellar clouds, planet-forming disks, and even exoplanet atmospheres, we can see physics and chemistry playing out exactly as they do down here on Earth. So, no matter what surprises life on other worlds may have for us, it must still utilize the same basic laws of physics and chemistry that apply on Earth. Based on this cosmic uniformity, scientists are already exploring what alternative biochemistries might look like.1 There are even studies of how photosynthesis might work on planets with very different kinds of suns.2
On the question of intelligence, things get shakier. That’s because there are so many steps needed for its development. Worse, we don’t know which steps are essential and which happened to be specific to how intelligence worked out on Earth. In dealing with the evolution of intelligence, however, we do at least have a principle we believe should be general across all planets. The genius of Darwinian evolution is its ubiquity. Darwin proposed that all life on Earth was shaped by the same set of simple processes: mutation, adaptation, and survival of the fittest. Simply put, whatever organism is best adapted to the environment will outlast its competition. It’s a principle that applies to everything from the first self-replicating molecules to modern, fully-formed biological organisms. It should even apply to future self-replicating robots if we ever make them.
So, when it comes to evolution on other worlds, this kind of uniformity should prove useful, particularly when we think globally in terms of biospheres. Darwinian evolution, in terms of population growth and competition in ecosystems, gives us a constraint for our ideas as we follow them to their consequences.
The science of sociology and the question of the formation of civilizations seem to be another story entirely. We cannot assume that sociological truths we’ve observed on our world will hold true across time and space. Do other civilizations have political parties? Do they worship a god or gods? We can tell stories about how an exo-civilization might organize itself, but our descriptions would always be just that—a story. Here, I am specifically referring to questions of their morality or economics or religion. Have they, for example, created institutions that value altruism over conflict, or conflict over altruism? Does the idea of institutions even make sense in their civilizations?
Unlike the foundational laws of physics and chemistry or the potential for Darwinian evolution to be cosmically general, it’s hard to see what kind of universal principles exist that would allow us to constrain something like alien economics. When it comes to sociology, I don’t believe such constraints exist.
So, while we are now in a position to begin building a science of exo-civilizations, the questions we can meaningfully take on must be limited. We need to avoid science fiction stories. That means speculation about whether civilizations are warlike or peaceful, or whether cultures focus on empire building or are content to stay at home, is out of bounds. Trying to answer questions about any of these dichotomies is close to a hopeless task. Extending our knowledge from the seen to the unseen requires something that keeps our theory building within nature’s possible bounds. No matter how far we want to reach, there has to be some ground for our feet to stand upon. For the time being, that means sticking with the physics and chemistry of planets (things like climate) and the parts of biology one can reasonably argue should be common. In developing our science of exo-civilizations, we should try to avoid questions about culture. That will be the challenge in building our astrobiology of the Anthropocene.
Of course, this strategy cuts out a whole lot of questions that many people want to know about exo-civilizations. For example: What are aliens like? Do they have two sexes, or twenty-three? Have they built a society on logic or on love? Are they traders or warriors? And of course: Do they look like us? If those are the questions you want to ask, I’m afraid you’re out of luck as far as our scientifically bounded theorizing is concerned.
But there is one specific kind of question about those civilizations that our science of exo-civilizations can address directly. By sticking to the laws of planets we learned through Carl Sagan, Jack James, Lynn Margulis, James Lovelock, and thousands of others, we can now ask the question that matters most to our project of civilization: How common is the Anthropocene? How often do civilizations trigger climate change on their planets? And, most important, how easy is it for a civilization to make it through its Anthropocene bottleneck?
OF PREDATORS AND PREY
The Adriatic Sea has fed Italy’s eastern shores for eight thousand years. From Venice in the north to Brindisi in the south, its warm waters have provided a livelihood for more than a hundred generations of fishermen. There are 450 different species of fish swimming in the Adriatic, many of which end up as food on Italian tables.3 But those tables have always been demanding. Human fishermen are the Adriatic’s top predator, and many of the sea’s species are currently in danger of collapse from overharvesting.
But the beat of fishermen’s oars or the buzz of their motors in the Adriatic has not been uniform across history. Conflict can slow the pace of fishing, as fleets of warships patrolling coasts make the work even more dangerous than normal. In World War I, the Adriatic became a battle zone. The new efficiency of mechanized navies gave Italy’s enemies a long enough reach that commercial fishing in the Adriatic almost ground to a halt.
For all its hardship, that lull in fishing proved to be an unlikely gift to science. By slowing the human draw on the Adriatic’s fishing stocks, a paradox surfaced that reshaped how biologists thought about animal populations, ecology, and the nature of their own work.
In the years immediately after the war, a young marine biologist named Umberto D’Ancona was working himself to exhaustion studying fish populations and their evolution. Through long, diligent work, D’Ancona amassed statistics on sales at fish markets in cities like Trieste, Fiume, and Venice, across the length of Italy’s Adriatic coast. His data bracketed the war years, beginning with 1910 and ending in 1923. Poring over the numbers, D’Ancona saw something that defied explanation.
During the war years, when fishing had been reduced, the number of predators such as sharks seemed to soar. This might have made sense if the numbers of prey fish, like mackerel, had also climbed, as D’Ancona had expected. More prey should mean more predators. But the numbers of prey fish didn’t rise during the war. Instead, they dropped. The statistics in front of D’Ancona told him that less fishing led to fewer prey fish and more predators. The young scientist puzzled over his paradox until, in desperation, he brought his biology problem to an unlikely consultant: the great mathematician and physicist Vito Volterra.4
Volterra was a world leader in solving hard physics problems. His work had touched on everything from the structure of crystals to the behavior of fluids.5 But Volterra’s reputation was not the main reason fate and D’Ancona brought him into the domain of biology. D’Ancona was also marrying the professor’s daughter. Luisa Volterra was herself a scientist, with a specialization in ecology—the biological study of populations and their environment.
Physicist Vito Volterra (third from left) developed the predator-prey model of population ecology for his son-in-law, marine biologist Umberto D’Ancona (far right). D’Ancona's wife, ecologist Luisa Volterra (daughter of Vito), stands next to him (circa 1930).
At the time Volterra took up the problem, mathematical “modeling” of the kind found in physics was not yet in the biologists’ toolkit. Biologists certainly dealt with statistics, but modeling is something different. Modeling is essentially a theoretical enterprise. It’s a process that begins by choosing a set of assumptions about how the world works. Those assumptions then get turned into mathematical equations, and those equations are what scientists mean what they talk about a model.
As we have seen in building climate models for Earth or Mars, the essential step in mathematical modeling is solving equations. Those solutions are descriptions of the world’s behavior over time. They are, therefore, predictions. So, whatever equations Volterra came up with for D’Ancona’s fish problem, their solutions needed to predict how the predator and prey populations changed with time.
Physicists have been making mathematical models ever since Newton devised his laws of mechanics back in the 1600s, giving physics its deeply theoretical emphasis. But biologists in the early twentieth century saw their work in a different way. The kind of modeling physicists routinely carried out didn’t seem up to the task of explaining the complexity of living systems and their interactions. As complicated as the orbits of planets might be, the complexity of a single cell, or even a simple food chain, puts astronomers to shame. For biologists, fieldwork always led the way.6
By the time Volterra began thinking about his son-in-law’s problem, however, things were changing. A movement had already begun to bring theory, in the form of mathematical models, into biology. That work had begun in the 1800s, when Pierre Verhulst of Brussels discovered what he claimed was a law of populations.7 Consider, for example, a few bacteria introduced to a pond. Their numbers will climb rapidly as each cell divides into two new “daughter” cells. The two daughter cells then divide, leading to four granddaughter cells. The process continues, yielding eight cells, and then sixteen, and so on. Soon, the bacteria population is skyrocketing. But it’s a process that can’t continue forever. Limitations on food and space mean that at some point the bacteria population reaches an environmental limit. That limit is called the environments’ carrying capacity. The population starts low, rises quickly, and then flattens out at the environment’s carrying capacity.
A century later, Volterra (and others) took theoretical biology further by creating what is now known as the classic predator-prey model.8 It begins with two equations. One tracks the prey population, which could be something like the number of bunnies in a forest. The second follows the predator population, which we could imagine as the number of wolves in the same forest. The important point for modelers to capture is that the two populations are tied together. The wolves eat the bunnies, and that changes the bunny population. But eating bunnies lets the wolves reproduce, adding to their population. So, the bunny population affects the wolf population, too. In these linked equations, there’s a part (a “term”) that describes how the bunnies get eaten by wolves, and another that describes how the wolves have more babies by eating bunnies.
In the language of math, the predator (wolf) and the prey (bunny) populations are coupled. They depend on each other. The two equations must be also solved together, which makes the problem tricky from a technical point of view. Volterra worked out this solution, and it showed him that the wolves and bunnies can end up cycling back and forth from high to low populations and back again. What was truly surprising, though, was the timing.
In an environment where both the bunny and wolf populations start low, the model predicted that only the prey begin increasing rapidly. The bunnies start reproducing first, and their numbers climb. The wolf population only begins increasing after enough bunnies are around to make them easy to find and catch.
Eventually, the bunny population peaks as the rapidly growing number of wolves starts having its impact. After that, the bunny numbers drop and they start to grow scarce. The wolf population, however, takes some time to feel the change. Only later do their numbers peak and then start dropping. Eventually, the wolf population gets low enough for the bunnies to recover, and the cycle begins anew.
What D’Ancona saw during the war was that the sharks (the predators) were still on the upswing, while the mackerel (the prey) were already past their peak and in decline. Volterra’s model predicted the lag between population peaks, so it explained why the shark numbers would be seen increasing while the mackerel were falling. In this way, Volterra’s theory—meaning his mathematical model—let D’Ancona get to the root of his apparent paradox.9 The theory revealed the essential biology of predator-prey interactions.
What emerged from the work of Volterra and other pioneers was a true form of theoretical biology. In this setting, theory doesn’t mean a hypothesis, like a detective’s notion of who committed a murder. Rather, in science, theory means a large body of knowledge resting on mathematical principles that have been thoroughly verified through experience. The theory of population biology (also called population ecology) that Volterra and others founded was powerful enough that it could be applied to an ever-growing range of problems. Today, population biologists, ecologists, and their compatriots use mathematical models to study everything from the spread of disease to the propagation of invasive species.10 Their approach would, eventually, even find its way to the study of human civilizations.
EASTER ISLAND, EASTER EARTH
Easter Island is a long way from anywhere. Located more than two thousand miles west of Chile and four thousand miles southeast of Hawaii, it’s an isolated outpost of land surrounded by seemingly boundless expanses of ocean. The skilled Polynesian sailors who colonized the Pacific thousands of years ago didn’t reach Easter Island in their long canoes until sometime around 400 CE. When they did, they found an island rich in fertile soil, as well as plant and animal life. It was a promising beginning to a story that would end in ruin.
When Dutch explorers discovered Easter Island on Easter Sunday in 1722, they found a “barren place with a few thousand people living in abject poverty and fighting over meager resources.”11 The island was devoid of trees, and the ground was covered only with unproductive scrub. But the huge stone monuments dotting the island and fashioned in the shape of human sentinels spoke of a very different past. Many of the “stone heads” were thirty feet high and weighed more than fifty tons. The silent faces of the monuments reflected a time when Easter Island had hosted a vibrant civilization with a population that may have peaked at more than ten thousand people.12 Whatever culture existed before the Dutch arrived, it was technologically advanced enough to carve the monuments from rock located at the volcanic core of the island and transport them across miles of rugged terrain.
The mystery of what happened to Easter Island’s civilization has haunted generations of writers and scientists. Erich von Däniken, in his 1973 bestseller Chariots of the Gods? went as far as to suggest an alien civilization was the only explanation.13 How, he asked, could the islanders have moved the massive stone monuments when there were no trees around to use as rollers? But ancient aliens were not required. The answer to Easter Island’s mystery turned out to be far simpler, and far more depressing.
There are no trees on Easter Island because the Easter Islanders cut them all down. They deforested their island in the building and transportation of those giant stone heads. In the process of deforesting the island, they also started a downward spiral that drove their civilization to collapse.
The iconic statues on Easter Island are evidence of a thriving civilization that collapsed before the Dutch landed there in 1722.
While there remains debate about the exact trigger for Easter Island’s fall, environmental degradation driven by the inhabitants’ own activity played an essential role. Easter Island serves as an object lesson for the interaction between an isolated, habitable environment and a civilization using that environment’s resources: they did it to themselves. The parallel to our current situation on Earth seems clear.
In his 2007 bestseller, Collapse, anthropologist Jared Diamond unpacked that parallel.14 His work explored the trajectories of a number of human civilizations that disappeared at the height of their vibrancy and power. Diamond’s examples included the Anasazi of the American southwest, the Maya, and the Norse colony on Greenland. In each case, the civilization overshot the carrying capacity of its environment. Their populations grew as the society became ever more ingenious at extracting resources from its surroundings. Eventually, the limits to growth were hit. A short time after running into those limits, each civilization fell apart. Easter Island was the poster child for Diamond’s story.
By the time Diamond brought historical examples of environmental collapse to the public’s attention, scientists had already begun the mathematical modeling of Easter Island’s fall. Using the same kinds of biological population models as those pioneered by Volterra and others, these researchers developed equations to explore the islanders’ trajectory from vibrancy to collapse.
It began in 1995 with a paper by environmental economists James A. Brander and M. Scott Taylor.15 Brander and Taylor set out two equations. The first described the change in the human population over time. The second described the change in the availability of the island’s resources over time. Just as in Volterra’s predator-prey models, the two equations were coupled. As the islanders used the island’s resources for food and technology, their numbers grew. The resources, like trees, were renewable, and the equations could describe them growing back at natural rates, even as they were harvested by the islanders. But when Brander and Taylor solved their equations for the coupled trajectories of both the human population and the island’s resources, their model tracked the islanders’ fate with a grim certainty.
As the population grew, the resources could not keep up. Overharvesting pulled resources down, and eventually, the island’s inhabitants went with them. Peaking sometime around 1200 CE, the human population of Easter Island then experienced a gradual die-off, ending with just a few thousand inhabitants left by the time the Dutch arrived. The mathematical model got the general trend in the history right.
Other researchers soon followed up on Brander and Taylor’s work. They changed the assumptions in the model by adding new terms to the equations or changing the form of the terms to reflect different kinds of interactions. A 2005 study by Bill Basener and David S. Ross16 looked at the problem slightly differently. They assumed that the island had a carrying capacity for humans, as well as for the island’s resources (like trees or animals). In their models, they then made the human carrying capacity explicitly dependent on the resources. As the resource levels declined, the ability of the island to host a human population would drop as well. When Basener and Ross solved these new equations for Easter Island’s history, they saw something different from the gradual die-off Brander and Taylor found. The population climbed to its peak and then dropped like a stone—a true collapse.
Theory building regarding the history of Easter Island continues, with new studies appearing each year. There are many open issues that researchers must struggle with, since some of the data about the island before the Dutch arrival remains open to interpretation. But the basic path of the islanders’ fate seems well captured in the models.
That success shows us the way forward for thinking about our own planetary fate in its proper cosmic context. What is true for an isolated island, its ecosystems, and its inhabitants should also be true for planets in the isolation of space.
A THEORETICAL ARCHAEOLOGY OF EXO-CIVILIZATIONS
In 1959, Carl Sagan took the greenhouse effect, a theory developed sixty years earlier for the Earth, and applied it to the distant planet Venus. In 1983, James Pollack and his collaborators took detailed models of dust storms on the distant planet Mars and applied them to Earth’s own climate after a nuclear war. In the midst of the current exoplanet revolution, astronomers are taking knowledge gained from studying Venus, Mars, and Earth and applying it to the habitability of distant worlds orbiting distant suns.
For the last five decades, our knowledge of planets as generic cosmic phenomena has exploded. Data from these different worlds has been cross-pollinated with our understanding of Earth, helping us to understand other worlds, both in their own right and in relation to our own. This cross-pollination is so robust that scientists are now creating detailed models of possible biospheres on exoplanets. They want to be ready with predictions when soon-to-be-completed telescopes give them next-generation views of exoplanet atmospheres.
But if we are already creating theoretical models of biosphere-harboring exoplanets, what keeps us from carrying out the same process for worlds harboring civilizations? If we ask the right kind of questions, nothing stands in our way; we can get started now. By uniting our understanding of planets with population ecology—in the spirit of Volterra and those who followed—we can take a first stab at exploring the coupled trajectories of civilizations and their planets as generic cosmic phenomena.
It’s a project that might be called a theoretical archaeology of exo-civilizations.17 Anything we do concerning exo-civilizations will have to be theoretical. This is true not only because we don’t have data, but also because our method will start from basic ideas about life and environments, as Volterra did in developing his predator-prey model. We want to let physics, chemistry, and population ecology guide us in unpacking the possible histories of exo-civilizations. Our goal with this theoretical archaeology of exo-civilizations is to see what could have happened to them, so that we can get a better handle on what might happen to us.
Given both the audacity and possible absurdity of anything calling itself a theoretical archaeology of exo-civilizations, let’s boil the idea down to its core elements.
Step 1: Other Civilizations, Other Histories. As the pessimism line indicates, unless the universe has a really strong evolutionary bias against creating civilizations, we are not the first. If we are willing to take the existence of those other civilizations seriously, then we will recognize that each will have its own history in terms of interactions with its host planet.
Step 2: It’s All about the Averages. We’re really interested in things like Drake’s final factor: How long, on average, does a technological civilization last? That means the results of a single theoretical model don’t really tell us much. What we need are statistics compiled by modeling a large number of exo-civilizations. Thanks to the pessimism line, we know what that means.
Scientists usually like to have more than a thousand data points for whatever they’re studying (this is true even in political polling). With that much data, quantities like averages make sense. So long as nature’s choice for the biotechnical probability is one thousand times greater than the pessimism line, a thousand exo-civilizations will have already lived out their histories across cosmic space and time. Given the already tiny value of the pessimism line, it’s not much of a leap to imagine that a thousand civilizations have already run their course. This would require a biotechnical probability of just one in ten thousand trillion (10–19), which is still much smaller than most historical pessimists have feared.
Step 3: There Is No Free Lunch. Now we enter the territory where our astrobiological view of planetary science and climate studies comes into play. In the public debate about sustainability, the focus is often on switching our civilization’s energy source from fossil fuels to something with less of a planetary impact. There is nothing wrong with such a goal, but the message often gets mangled in public debate from “less impact” into “no impact.”
If we take the astrobiological view and start thinking like a planet, we see there’s no such thing as “no impact.” Civilizations are built by harvesting energy and using that energy to do work. The work can be anything from building buildings to transporting materials to harvesting more energy.
Without technology, each human being gets one human being’s worth of energy each day. But with technology, we vastly expand the energy at our disposal. The average American uses the equivalent of about fifty servants just to power their home.18 If we add in the energy needed for driving, flying, and other activities, the number of virtual servants gets much, much higher. Since this is just a matter of physics, what’s true for us in terms of energy, power, and work must be true for any civilization-building species. The whole process of building a technological civilization is really an exercise in harvesting energy from the surroundings—in other words, from the planet.
So you can’t build the kind globe-spanning, energy-intensive civilization we’re interested in without having some impact on your planet. In fact, the laws of physics demand that you have an impact. Specifically, the Second Law of Thermodynamics is the culprit.
The Second Law tells us that energy can’t be perfectly converted into useful work. There is always some waste. So any civilization-building species on any planet, using any form of energy, must produce waste. As that waste builds up, it turns into feedbacks on the planetary systems. From this perspective, the CO2 produced by our burning fossil fuels can be seen as a kind of waste product of our civilization building. So, while the waste can take many forms, all of it will affect the planet. The states of the atmosphere, oceans, ice, and land will all change as the waste accumulates. That’s the real scientific story of climate change and the Anthropocene.
Now, you might counter with the argument that civilizations more advanced than ours will find ways around the Second Law. Most physicists will tell you, “Good luck with that.” The Second Law is baked into the structure of the universe, and being able to skirt it entirely is very unlikely.
But what capacities a highly advanced civilization might possess is an extremely important question for our theoretical archaeology project. It’s so important, in fact, that our archaeology of exo-civilizations is designed explicitly to avoid speculating about it. And that leads us to the next step.
Step 4: Planets Come with a Limited Number of Energy Sources. In building our archaeology of exo-civilizations, we are going to focus explicitly on young technological civilizations. That means civilizations at our stage of development. This focus makes sense for two reasons. First, the whole point of this enterprise is to see what we can learn by treating our predicament as a general and generic phenomenon. The challenge humanity faces in the Anthropocene would not be so compelling and existential if we already had warp drives and other super-technology. Understanding our immediate fate is one good reason to keep our thinking focused on young civilizations. But the emphasis on youth is also essential for creating a project with strong scientific constraints.
One of the greatest impediments to thinking about exo-civilizations (or our own deeper future, for that matter) is technological progress. How can we anticipate what kind of technology a civilization that’s a million years older might have at its disposal? Societies that mature might have found entirely new forms of energy that come from thin air. How can our theoretical modeling of exo-civilizations account for unknown sources of energy we haven’t discovered?
The answer is, it can’t. But luckily, it doesn’t have to.
The development of technology is like climbing a ladder. You can’t make a steel blade until you know how to make an iron blade. The Babylonians simply didn’t have the capacity to build the metal-alloy components of a modern wind turbine. Each civilization must climb up the ladder of technological sophistication as it discovers the physical and chemical principles of the world around it.
For our project, that means a young civilization will have a limited number of energy sources available. Crucially, we know what those forms are. The laws of physics, chemistry, and planetary evolution tell us what resources might be at the ready for an intelligent species building its way up the technological ladder. Here is a pretty complete list of the energy resources a planet might offer:
•Combustion. This means burning stuff. It could be fossil fuels that are burned if the planet went through the right kind of geologic epoch, or it could just be biomaterials, like wood on our world.
•Hydro/Wind/Tides. If the planet has fluids or gases flowing on its surface, then those movements can be tapped to generate energy.
•Geothermal. Heat from the planet’s interior can also be captured and used to do the work of civilization building.
•Solar. Sunlight can be trapped in both low-tech (heat) and high-tech (electric current) ways.
•Nuclear. The energy locked up in atomic nuclei can be used as long as there are reserves of radioactive elements like uranium around. Nuclear energy is obviously higher on the technological ladder than other modes of energy harvesting, but given that our society has made use of it, it’s fair to think that others might as well.
The specific conditions on each planet will ultimately determine the mix of energy modes available to a civilization evolving there. Geothermal may be more favorable on some worlds, while wind may be more easily tapped on others. The main point for now is that the list above hits almost all the choices. Other than imagining exotic planets with special magnetic fields or continuous lightning conditions, what’s on the list above is all that exists. Adding new energy sources other than those we’ve listed requires inventing science fiction stories about discovering “new physics.”
Step 5: Know the Impact. Since we can list the different energy sources available to a young civilization, we can also calculate the planetary impact of their use. If this sounds like science fiction, remember that way back in 1903, Svante Arrhenius carried out exactly this kind of calculation for the Earth and combustion (that is, the burning of fossil fuels). Arrhenius knew the composition of the Earth’s atmosphere, and he could calculate the impact of using coal. This impact was the production of CO2, and the change it produced was an enhanced greenhouse effect.19
So, for civilizations powered by combustion, we already know how to model their impacts on their planets. All that’s needed is to account for the potential differences in their host planets’ properties, which will include things like the composition of the atmosphere and orbital location in the habitable zone.
What about the impact of other energy sources? For some cases, the calculations have already been started. A study by scientists at the Max Planck Institute in Germany looked at the global effects of wind power. Wind turbines work by pulling energy out of smooth, large-scale flows of air and turning it into electricity. But in the process, they leave choppy, turbulent airflows downstream. The German group found that extracting energy from wind power on a scale massive enough to power our current civilization would leave a global imprint akin to mild global warming. Even wind, the darling of renewable-energy harvesting modalities, has a planetary cost (though far lower than fossil fuels).20
Because we have a deep understanding of the physics and chemistry of each of the energy sources listed above, it doesn’t take a quantum leap in science to calculate how their use will produce feedbacks on a planet other than our own. For each energy source a civilization might harvest, we have the information necessary to calculate the associated planetary cost. With that capacity, we reach the final step in the path to our theoretical archaeology of exo-civilizations.
Step 6: Turn the Crank. Given steps 1 through 5, we now have a recipe for calculating exo-civilization histories. We begin by creating a model for the interaction of a young civilization with its planetary environment. This model will come in the form of equations predicting how the civilization’s population and its host planetary systems change with time. As in the predator-prey model, the equations will be coupled. There will be an equation describing the change in the planetary systems (such as atmosphere) and an equation describing the changes in the civilization-building population. Each equation will have terms that describe the feedback from planet to civilization and civilization to planet. It’s worth mentioning that to do this job well, we’ll need more than just two equations, because we’ll probably need to track different resources and their use, along with their effect on the different planetary systems like oceans, ice, and so forth. But for now, we can stick with just “the planet” and “the civilization.”
In general, the civilization will use its energy sources, and the waste from those energy sources will push on the state of the planetary systems. As the planetary systems shift based on the feedback, the civilization will either thrive or be stressed, as reflected in how their population changes. Because the coupling will be complicated, we won’t know what to expect until we’ve solved the equations making up the model.
Doing this once doesn’t tell us very much. What we are interested in is Drake’s final factor: the average lifetime of civilizations. In order to calculate an average, we will have to run our models many times for many different kinds of planets. In a sense, by running the experiment in civilization building over and over, we will create our own mini-version of the universe. Some of the model runs will begin with planets that are close to the inner edge of their stars’ habitable zones, where they’ll be particularly susceptible to enhanced greenhouse warming. Some will be farther out. Some of our model runs will have planets with atmospheres that have less oxygen than ours, while others will have more oxygen. Some will begin with civilizations using wind power, and others will begin with civilizations using geothermal. You get the picture.
In the end, we will have to “turn the crank” and run tens of thousands of models, each with different starting conditions. That might seem like a lot of work, but modern computers are fast.
PATHS TO PROGRESS, ROADS TO HELL
Carrying out a theoretical archaeology of exo-civilizations correctly will be demanding. It will require input from fields as diverse as atmospheric science, geology, energy science, and ecology. To create realistic models, we’ll have to get the physics, chemistry, planetary science, and ecological interactions right in terms of what we build into the models. That is going to be a long and interesting project.
But even as we build our way toward that goal, we can take some initial steps now. These first explorations can give scientists the lay of the astrobiological exo-civilization landscape. In the fall of 2016, a team of us went on this kind of scouting mission. The result was simultaneously thrilling, hopeful, and possibly a little depressing.
Our team included Marina Alberti, an urban ecologist from the University of Washington. A native of Italy, her passion is how evolution is already responding to the Anthropocene. Marina studies urban environments and how new species are being created in the midst of our vast project of planetwide city building. Axel Kleidon was also part of the effort. Axel is also an innovative thinker who works at the Max Planck Institute for Biogeochemistry, developing new ways to look at the Earth as a single thermodynamic system, like a giant, planetwide steam engine. Finally, there was Jonathan Carroll-Nellenback. Jonathan was my graduate student years ago and now works with me as a senior computational scientist at the University of Rochester. His talent for theoretical work is pretty remarkable. Sometimes I’ll bring Jonathan a problem in the morning, and by the next day he’ll bring it back, fully solved and displayed in beautiful graphics.
Together, we formulated a model for the evolution of a civilization with its planet. The equations were pretty simple. We weren’t trying to capture the details of Earth or of any other specific planet. Instead, our aim was to describe the interaction of civilizations and planets in the most general way possible, which would serve as a first step toward doing something more detailed and realistic.
In our approach, the population and the environment were linked via an energy resource. The planet supplied the energy resource, and the civilization used the energy resource. Greater energy use meant a larger population on the one hand, and greater environmental change on the other. Greater environmental change lowered the planet’s carrying capacity for the civilization, which should lead to lower populations.
Along with these features, we also included a specific mechanism to describe how the civilization might respond to changing conditions on its planet. For the sake of simplicity, we imagined that the planet had just two kinds of energy resources. One resource had a high planetary impact (as fossil fuels do), while the other had a low impact (as solar energy does). Here, high and low impact reflected the degree to which using the energy source forced the planetary environment to change.
Once the planetary environment was pushed past some predefined point, the civilization switched energy resources. You can think of this in terms of the planet’s temperature. Once the planetary temperature rose to the specified value, the civilization stopped using the high-impact energy source and switched to the low-impact source.
Using this strategy in the models gave us a specific and simple way to boil down the civilization’s sociology. We didn’t want to try and model how they’d recognize and act on their Anthropocene. Instead, it came down to the planetary temperature that finally gets the civilization to do something. Since that was just an input, we could change it from one run to another and see how history played out for “smart” civilizations and “dumb” ones. Either the civilization acted early, when their planet’s temperature had just started to rise, or they acted late. While we couldn’t model the sociology of how they made that choice, we could model the choice’s physical consequences. Would acting early save them? Would anything save them?
So, what did the model tell us?
Our exploration of the exo-civilization/planet system yielded three distinct trajectories. The first—and, alarmingly, most common—was what we called “the die-off.” As the civilization used its energy resource, its numbers grew as expected (see page 196, graph A). But the use of the resource pushed the planetary environment away from its initial state. As the evolution of the coupled civilization/planet system continued, the population rose sharply beyond what the environment could sustain. The population, in other words, overshot what the planet could support. A big reduction in the civilization’s population followed, until both the planet and the civilization had reached a steady state. After that point, neither the population nor the planet changed anymore. A sustainable planetary civilization was achieved, but at a considerable cost.
In many of the models, we saw as much as 70 percent of the population die before a steady state was reached. Imagine seven out of every ten people you know perishing because of global climate catastrophes. It’s not clear how large of a die-off a complex technological society could handle without falling apart. During the period of the Black Death in the fourteenth century, Europe lost between 30 and 50 percent of its population, but managed to revive. Medieval Europe, of course, wasn’t highly technological in the modern sense, nor as isolated as a planet in space would be.
The second trajectory class we found was one we called the “soft landing” (see page 196, graph B). The population grew and the planet changed, but the models showed a smooth transition to a steady state after an early switch to the low-impact energy resource. Eventually, the civilization came into equilibrium with its planet without a massive die-off.
Four kinds of trajectories for exo-civilizations and their planets discovered from mathematical models.
The final class of trajectory was the most worrisome: full-blown collapse. As in the die-off models, the population initially grew swiftly. In this case, however, the speed of planetary change pulled the planet’s carrying capacity down so fast that the population plummeted all the way to extinction.
One of the most remarkable aspects of this class was that the collapse was inevitable. One would think that switching from the high- to low-impact energy source would make things better. But for some trajectories, it didn’t matter. If we used only the high-impact resource, the population reached a peak and then quickly dropped to zero (graph C). If we allowed the civilization to switch to the low-impact version of an energy resource, the collapse was only delayed. The population would start to fall, then appear to stabilize, and finally, suddenly, rush downward to extinction (graph D).
The collapses that occurred even when the civilization did the smart thing demonstrate an essential point about the modeling process: it can surprise you. Because the equations representing the model are complex, unexpected behavior can happen. These are consequences you wouldn’t have thought of if you hadn’t done the work of cranking out the solutions.
Only after you study the behavior seen in the models do you understand what happened. Remember that our simplified models were tracing the development of a civilization and its planet together. In the case of the delayed-collapse trajectories, we were finding scenarios that showed us that switching from a high- to low-impact energy resource won’t matter if the change is made too late. Even though the civilization in our model recognized its entry into an Anthropocene-like transition and switched energy sources to make things better, the planet was already heading into new climate territory. Once the ball got rolling, the planet’s own internal machinery took over. It wasn’t coming back to the original climate state, and it took the civilization down with it as it ran away into a new state.
In these cases, the planetary environment’s own dynamics were the culprit. Push a planet too hard, and it won’t return to where it began. We know this can happen, even without a civilization present, because of what happened with Venus and its runaway greenhouse effect. Our models were showing, in generic terms, how a civilization could push a planet into a different kind of runaway through its own activity.
The work that Jonathan, Marina, Axel, and I did showed us some of the basic ways a civilization and its planet might change together. It was good that we saw that long-term sustainable versions of the planet-plus-civilization system were possible. But the warnings were there as well. The self-perpetuating feedbacks that drove some civilizations to collapse, even after they made the smart choices, were particularly sobering.
THE FINAL FACTOR
It’s reasonable to ask what this archaeology of exo-civilizations really tells us about reality. Aren’t these models just mathematical toys? Isn’t it true that we have not a single instance of a civilization other than our own to make comparisons with? Answering these questions will help us see what can be gained by taking exo-civilizations seriously as subjects of scientific inquiry. It will also help us see what’s at stake for us as we try to use this astrobiological perspective to understand our choices about our own project of civilization.
Models and Reality: The trajectory of the Anthropocene shown with real data for world energy consumption, CO2 concentration, and global population for the last 10,000 years.
It is absolutely true that models and reality are two different things. A model is a simplification, like a skeleton without the muscle and skin. But looking at just a skeleton will tell you a lot about the animal. That is how we know about dinosaurs. More to the point, as we move forward, our models will be based on ever more sophisticated versions of what we already know about how planets work. They are, and will be, built on ever-stronger skeleton frames of physics and chemistry—in other words, the laws of planets. In that way, they are far more than mere imaginative toys.
The models allow us to go beyond fiction. By relying on the laws of planets, they capture key aspects of reality. That means they have their own logic. They have their own stories to tell us that we would not see without them. It’s one thing to argue over what you think will happen when a civilization on a distant planet becomes technologically sophisticated. Your friend might have a different opinion, and that’s an all-night argument waiting to happen. But it’s something entirely different to spin up the math and let it see into the complexities that elude us. Instead of mere opinion, we can let the model show us how the universe might behave. The realistic constraints models place on their stories give those stories scientific value. It grounds them in the realm of the possible.
All the research we’ve explored in this chapter constitutes just a first step. It’s an outline of what this kind of enterprise will look like as we devote more time and effort to the endeavor. The stories we’ve told here are just the first of many, and they will grow more precise as our understanding increases.
The next step will be to build far more realistic models and use them to explore a much wider range of realistic cases. After running these models for hundreds of thousands of different situations, we will have the simulated trajectories—the histories—of hundreds of thousands of inhabited worlds.
A planet that lives close to the inner edge of its habitable zone might be so highly sensitive to runaway greenhouse warming that its civilization barely has time to progress before it faces its own version of the Anthropocene and collapses. Another world, farther out from its star, may be less sensitive to planetary change but have a civilization that refuses to recognize the change until the die-off has already begun. A different species on a different world could manage to build its project of civilization using only lower-impact forms of energy and make a gentle soft landing to a sustainable state that lasts thousands of millennia.
What part of these stories matters to us? The answer to that question is simple: Drake’s final factor. With trajectories for millions of simulated planets and civilizations in hand, we can calculate an average lifetime. How long, on average, does a civilization last?
Consider, for a moment, what that single number would tell us.
If the average lifetime of exo-civilizations is two hundred years, then we are in big trouble. If we find most model civilizations collapse after just a few centuries, the implication would be that civilizations like ours just don’t work well on a planetary scale. A short average lifetime would mean that the universe doesn’t do sustainable civilizations. The lesson would be that we humans are threading the eye of a needle with the Anthropocene and don’t have much room for error in our choices. In that case, it may already be too late.
If the average lifetime of civilizations emerging from our models were tens of thousands of years, that would be good news. It would mean it’s not too hard for any civilization to make it through the bottleneck of an Anthropocene.21 There would be lots of different strategies for reducing our impact on the planetary systems that work. It would mean we have lots of wiggle room. We could make mistakes and still recover.
In this way, a single number from our archaeology of exo-civilizations—the average civilization lifetime—would have profound implications for our own future and our actions in the present. It would let us see what might be coming. And with that knowledge, our understanding of the choices we face would become deeper, richer, and be based on some wisdom.
Beyond the question of the average lifetime, we could also use the models to see exactly what choices are most likely to save us. Once we have a full suite of trajectories, we can ask what explicitly led some to civilizations to achieve planetary sustainability and others to collapse. Like a doctor looking for a cure by studying the most pathological cases of a disease, we can see what common factors drove the civilizations that died to their fate. The models will have a lot to teach us that we can’t see now with the tunnel vision of just our planet and just our own uncertain future.