The first statewide carbon tax in the United States almost certainly isn't going to happen. Washington votes by mail, so it ain’t over yet, but the No side of Initiative 1631 has just over 56 percent, with more than two thirds of the votes counted. It doesn’t look good.

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The WIRED Guide to Climate Change

That’s a disappointing end for a bill that some environmentalists and journalists had held out as a bellwether. Success would’ve told politicians and policymakers that, yes, Americans were finally ready to pay a little more money to save the planet. Initiative 1631 was technically a fee, not a tax; It would’ve charged many emitters $15 per metric ton of carbon, increasing every year until emissions declined—and the money would pay for green infrastructure like clean power generation, environmental remediation, and projects in communities most affected by pollution. (Polluters might well have passed on that price to consumers.) On the yes side: The Nature Conservancy, Bill Gates, REI, and a coalition of political groups. On the no side: $31 million of oil company money.

It would’ve been the first major carbon tax in the US, but not in the world. Finland was first, in the 1990s. They’re common throughout Asia and Europe. In Canada, British Columbia has had one since 2008, and its outcomes have reportedly been good—more jobs, reduced emissions. The US is the outlier here, though the idea has floated up at the federal level in the US, too—most recently from Carlos Curbelo, a Republican representative from Florida. (Washington state rejected a straight carbon tax in 2016; the money it raised was supposed to go back to taxpayers as rebates.)

A carbon tax is the sort of thing that economists have mostly agreed is obvious—greenhouse gases are an “externality,” a damaging side effect that causes harm to the environment but isn’t included in the price of a carbon-based economy. It’s possibly, theoretically, to figure out how much more damage an additional unit of pollution causes; those are so-called marginal damages. “In textbook economics, you set the price equal to the marginal damages, and that gives you a cost-efficient reduction in the damages caused by the externality,” says Marc Hafstead, director of the Carbon Pricing Initiative at Resources for the Future. It’s a little more complicated than that, Hafstead allows, because emissions are global and it’s hard to pin down exactly what the marginal damages are, but the basic idea is, if you tax emissions, people will emit less to avoid paying the tax wherever they can.

Washington voters seem to agree that the place where it’s most efficient to reduce emissions is “nowhere.” Most Americans believe global warming is real, people are a cause, and someone should do something. But on that last thing, they may not agree who or what. Opponents of the Washington bill argued what opponents of taxes usually argue: not enough oversight over how the money would be spent, wouldn’t achieve the goal it set out to, wasn’t fair. “It acts as a regressive tax, hurting those who can least afford to pay more, and it’s going to be ineffective at reducing Washington’s greenhouse gases,” said the No on 1631 campaign spokesperson Dana Bieber on election day. (A carbon tax doesn’t have to be regressive; that’s the sort of thing economists and policymakers fight about, but 1631’s supporters worked with communities that might have taken a harder hit to satisfy their concerns, and arguably the public health benefits would outweigh any expense.)

On its face, 1631’s apparent defeat was the capstone on a pretty bad day for environmental legislation. Arizona voted against a harder shift to renewable energy. (California tech billionaire Tom Steyer spent almost $18 million trying to put that one over the top; the local power company spent more.) Nevada said yes to renewables but declined to break up the state energy monopoly. Colorado voted to expand oil and gas drilling. Curbelo, the climate-minded Florida rep, lost his re-election bid. The planet is still burning.

From a political perspective, all of that suggests dealing with climate change will require strong national leadership. About which, uh oh, since President Trump doesn’t believe human beings cause climate change and lots of big Republican funders are tied to carbon-emitting industries.

Still, you could choose to look at your glass of petrochemicals as being half full. “Ballot measures are often susceptible to misinformation and lots of out-of-state money pouring in, and there are limitations on what a ballot measure can cover,” says Dylan McDowell, deputy director of the National Caucus of Environmental Legislators, a group that helps state legislators enact climate laws. “State legislation is more able to deal with something as complex as carbon pricing.”

Thanks to Democratic takeovers of governorships and state houses in 2018, that’s now more likely. New York, Colorado, New Hampshire, Maine, and Minnesota now have pro-environment majorities. Massachusetts is moving toward carbon pricing; Oregon legislators will probably vote on a cap-and-trade law next year. The governors-elect of Illinois, Colorado, and New Mexico all campaigned on renewables. And California still has its cap-and-trade system for carbon, and a new governor fired up to head into combat with the president. So the state level may still be a place for climate legislation.

Those state legislators sometimes become national policymakers. NCEL’s McDowell points out that his group worked closely with Iowa congresswoman-elect Abby Finkenauer when she was in the legislature. “One of the amazing takeaways is that the urgency we feel about taxing climate change has actually increased since we began the campaign,” Mike Stevens, state director in Washington for the Nature Conservancy, said on election day. Washington’s carbon tax might not be the first in the nation, but someone’s will be.

Since 2016, IBM has offered online access to a quantum computer. Anyone can log in and execute commands on a 5-qubit or 14-qubit machine located in Yorktown Heights, New York, from the comfort of their own home. This month, I finally tried it—nervously. I did not know what I was doing and worried I might break the hardware. “You won’t mess anything up,” IBM physicist James Wootton assured me via Skype.

I chose the 5-qubit machine. The online interface resembles a musical score consisting of five horizontal lines, one line corresponding to each quantum bit, or qubit. Qubits, the basic building block of a quantum computer, are pieces of hardware that represent numbers, just like the transistors in your computer—except they obey the bizarre laws of quantum mechanics. Designs vary, but IBM’s qubits consist of tiny circuits made of superconducting wire, kept in a refrigerator very close to absolute zero. The circuits can only hold information at low temperatures.

The potential of quantum computing lies in the weird behavior of these tiny circuits. If a transistor was a Lego, a qubit would be a blob of slime: the two components follow completely different rules, and can be used to build entirely different structures. For example, the circuits can be programmed into a delicate quantum state known as a superposition, where they are equal to neither 1 nor 0, but some combination of the two. Qubits offer a fluidity inaccessible to ordinary computers, which box the world in binary. Researchers are excited about this new capability—but don’t quite know how to take advantage of it yet.

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The WIRED Guide to Quantum Computing

As the conductor of my quantum orchestra, I decided what each little superconducting circuit did by dragging brightly colored icons representing commands onto its corresponding line at random. I clicked “Run.” The computer in New York obeyed, applying a cacophony of microwave pulses to the qubits to change their values. After about a minute, IBM sent me an automated email with my computation results. I examined the final value. Four qubits equaled 0, while a fifth one hovered in a superposition state that was 98 percent equal to 0, and 2 percent equal to 1.

“Of course, running something comprehensible requires you to read the user guide,” Wootton had told me.

It’ll take more than that. But the app, dubbed “The IBM Q Experience,” has already executed more than seven million quantum programs, and they aren’t just random gobbledygook like mine. Legit researchers, many unaffiliated with IBM, have published more than 120 academic papers using it.

The app is part of a larger effort to boost literacy in quantum computing. Other companies have also been releasing open-source software packages to reach out to the masses—or more probably, nerds with niche interests. They hope that a diverse pool of users can guide them toward more creative uses for the machines. “Anyone who is an enthusiast can play with these tools,” says physicist Nathan Killoran of Xanadu, a quantum computing startup based in Toronto. This November, Xanadu released an open source library called PennyLane that is compatible with IBM’s hardware. “The more people get into it, the quicker ideas can develop,” says Killoran. “And I think the best way to do that is to crowdsource the ideas.”

There’s a learning curve, for sure. The packages, predominantly written in Python, strive to make the software as user-friendly as possible with detailed tutorials and interactive apps. But to really exploit the computer’s quantum-ness, you have to develop an intuition about how qubits flow in and out of superposition and interact with each other via bizarre properties known as entanglement. “We try to ease the transition as much as possible,” says Killoran. “But at some stage, people do have to learn a little bit about the quantum side of things.” Users need some specialized technical knowledge, much like the twentieth-century operators of vacuum tube computers did.

Still, by letting everybody in, researchers are hoping that somebody will finally crack the mystery of quantum computing. Because despite the hype—it’ll break modern cryptography! It’ll design super-efficient batteries! It’ll reinvent the fertilizer business!—quantum computing’s advantages over conventional computing are still purely theoretical. The industry’s reach goals require powerful hardware made of millions of error-free qubits that will require a few decades to develop. So far the biggest device, announced Tuesday by Maryland-based startup IonQ, purportedly contains 160 error-prone qubits.

But researchers think these angsty teenage devices still have potential. Google, for example, has a 72-qubit quantum computer that it plans to make available to outside researchers. One of its projects is to promote the use of its computer in drug discovery and materials design, says Google chemist Jarrod McClean. To that end, the company released a software package this year called OpenFermion. Experts think that quantum computers should be able to simulate chemistry more accurately than conventional computers, since electrons, atoms, and molecules obey the same quantum laws of superposition as qubits.

Companies also want artificial intelligence experts to start using their computers. Quantum computing researchers suspect, though they don’t know for sure, that their new devices could speed up machine learning algorithms or improve their accuracy. Because quantum computers obey different mathematical rules, they should be able to identify different patterns in data sets, says physicist Maria Schuld of Xanadu. Last month Xanadu released PennyLane, a software package meant to make it easier to run machine learning algorithms on a quantum computer.

Software packages like Xanadu’s and Google’s are compatible with multiple quantum computing architectures. That’s because it’s still unclear which company’s hardware will reign supreme, says Google physicist Dave Bacon. Google and IBM both use qubits made of superconducting circuits; IonQ’s computer is made of single ions placed on a chip; Xanadu is trying to make qubits out of individual photons of light. Quantum software developers have had to remain open-minded about rival companies’ hardware.

Besides, the hardware is far from complete, says Schuld. It has a symbiotic relationship with its software: hardware experts can adapt their machine designs to better suit the algorithms with the most potential. At the same time, the limitations of the hardware inform how software developers build their tools. “[Software developers] can predict what’s interesting for the hardware people to try, and the hardware people can say, ‘ahh, stop, we can’t do this easily,’” she says.

To encourage people to use the code, IBM and Xanadu also run contests for the best projects that use their tools. What are they looking for? They’re not picky: Xanadu’s contest is offering $1000 for “[a]lmost anything,” they wrote in a publicity post. They do highlight one particular area of need: communications projects that explain what quantum computing is.

Remember all those classics you devoured in comp-lit class? Neither do we. Research shows that we retain an embarrassingly small sliver of what we read. In an effort to help college students boost that percentage, a team made up of a designer, a psychologist, and a behavioral economist at Australia’s RMIT University recently introduced a new typeface, Sans Forgetica, that uses clever tricks to lodge information in your brain. The font-makers drew on the psychological theory of “desirable difficulty”—that is, we learn better when we actively overcome an obstruction. (It’s why flash cards create stronger neural connections in the brain and are a better method for recalling facts than passively studying notes.) Sans Forgetica is purposefully hard to decipher, forcing the reader to focus. One study found that students recalled 57 percent of what they read in Sans Forgetica, compared with 50 percent of the material in Arial, a significant difference. No word yet on the retention rate of Comic Sans.

Mind the Gap

When presented with incomplete visual information, like the random gaps in Sans Forgetica’s characters, our brain fills in the missing bits. “They pique your attention and slow down the reading process,” says Stephen Banham, one of the font’s developers.

Seek Balance

While breaking some design rules creates desirable difficulty without sacrificing legibility, further futzing with the font—like one early prototype that incorporated back-slant, gaps, and asymmetrical letters—caused recall rates to ­plummet.

Lean Back

Your brain isn’t used to seeing sentences tilt to the left—it’s a typographic faux pas. It takes you a split second longer to recognize words in Sans Forgetica’s 8-degree back-slant, triggering deeper cognitive processing.

Use Responsibly

Reading an entire textbook in Sans Forgetica would be migraine-inducing. Instead, the font is meant to be used like a highlighter to emphasize important bits of information. A Chrome Extension lets you transform any section of online text into the typeface.

Make an Impression

Though Sans Forgetica was originally devised to give students an edge on exams, it’s since been sought out by brands—like an ad campaign for a Hungarian pharmaceutical company—to more effectively worm information into your brain.

There are so many real-world physics problems involved in running. Lots of physicists have been inspired, for instance, by the crazy-fast speeds of Usain Bolt. Just take a look at this paper, "On the performance of Usain Bolt in the 100 m sprint" (European Journal of Physics), in which the authors examine the motion of Usain in one of his sprints.

But what if you want to look at more … unrealistic running? Or model running in situations you shouldn't test in real life—like running at the pool? In order to explore these situations, you'll need a physics model for running. Remember that science is all about building models, right?

Let's say I want to build a model that shows the following real-world aspects of running:

• A runner hits the ground and then is off the ground during part of the motion (both feet off the ground).
• A runner accelerates but reaches some maximum speed.

It's fairly easy to fit a mathematical equation to a runner's motion. But what if you want to go a little bit deeper than just an equation (so we can change stuff)? One common idea is to assume there is a frictional force pushing the human forward and a velocity-dependent drag force pushing against the motion. As the speed of the human increases, so does the drag force, until you get to a "terminal velocity" where the two forces are balanced.

I don't like this approach (even though you can get it to fit the data fairly well). If air resistance were really the thing that limits running speed, then it would seem like you could go super fast on a stationary treadmill (you can't). Also, a tail wind would provide a huge boost (it only gives a tiny boost). Finally, you could run at crazy-fast speeds in a vacuum (who knows about this one).

Instead of the air-resistance model of running, I'm going to go with the following assumptions:

• A human is basically a point mass that is just plain projectile motion in between contact points with the ground.
• During this time off the ground, the human has to switch leg positions (move back leg forward), and this motion takes a minimum amount of time.
• During contact with the ground, a human can only exert some maximum force. Note, this is from an excellent paper, "The biological limits to running speed are imposed from the ground up" (Journal of Applied Physiology).
• Also from the same paper, the contact time with the ground depends on the running speed. The faster you run, the shorter the ground-contact time.

So why would this model work? Let me start with a diagram. Suppose a human is running and in contact with the ground (not during the stride part of the run).

Let's step through the important parts of this bouncing-ball model of a running human. Suppose the ball starts above the ground and is moving just a little bit (or just falling down—it doesn't matter). When it hits the ground, there has to be an upward force to change the ball's momentum so it doesn't keep moving down, but instead starts to move up and get off the ground again. I know the required vertical velocity of this ball after it impacts the ground because the ball has to stay in the air for a set amount of time (the stride time) so that its non-existent feet can switch from front to back.

That might seem all fine and dandy, but here is where the model comes into play. There is some maximum force that the floor can push up on this human-ball, and there is some maximum contact time over which the force can act on the ball. Let me remind you of the momentum principle.

As the running speed increases, the contact time decreases, meaning you need a greater upward force to get the ball in the air. Since there is some maximum contact force, more upward force means less forward force to increase the speed. At some point, the contact time gets small enough that all of the force has to be in the vertical direction to get enough air time. There will no longer be a forward-pushing force, and the horizontal velocity becomes constant.

OK, now I'm going to build this model. However, I need a few pieces of data. These are either rough guesses or based on the article in the Journal of Applied Physiology linked above.

• The maximum force on the ball is between 3 and 4 times the weight of the person.
• The stride time is about 0.3 seconds (the time the feet are in the air).
• The time of contact with the ground depends on the speed. I have an equation based on contact time vs. speed from the above article (using real data).

To get this to work the way I want, I have to add one more aspect to my model. For the horizontal force, only part of this (only 20 percent—random choice) goes to increasing the speed of the human-ball. I will assume the rest of this horizontal force is used to accelerate the legs so they can move back and forth in a motion that most people call "running."

You have waited long enough; here is the code with my bouncing human-ball running model. Just click Play to run it. Feel free to edit the code in whatever way you like.

Yes, it's not the prettiest code, but it seems to work. I added some comments so you can figure out how it works. Also, notice the parts where you can change the parameters (like stride time or maximum force) to see what happens. Play around with it—it's fun!

The animation isn't that useful, so how about a graph? Here is a plot of the horizontal speed as a function of time. Here is the code with graph stuff in case you need it.

That graph makes me happy. Why? First, it shows the human-ball reaching some maximum running speed. With the parameters I'm using, the running speed is around 9.5 m/s (21 mph). Just for comparison, the human record speed is 27.8 mph. Second, you can notice that the speed doesn't increase continuously. How can you increase your speed while you are in the air in between strides? You can't. You can clearly see that speed increases only during the contact times.

So, what's next? Well, I made this model because I want to use it for something else. In the meantime, you can use it for whatever you like.

Everyone has probably played with dominoes. At some point in your childhood, chances are that you tried this game: Line up a bunch of dominoes in a row and then knock one over. Soon the rest of the dominoes topple in a chain reaction. I'm not sure why this is fun, but it is.

But how about something even cooler? What if you have a small domino knocking over a bigger domino? This is indeed possible. But is it possible to ramp up the domino size so much that a huge domino could tip over and crush a car? This was the goal in a recent episode of MythBusters Jr., where I am the science consultant.

Physics research has shown that a domino can knock over another one that is 1.5 times taller. OK, this is what makes physics so cool. We don't just study protons and black holes. Physicists also write papers about dominoes. Check out this paper on arXiv.org. Also, there is this much older paper (from 1983) in which someone described this taller domino crash as a way to demonstrate a nuclear chain reaction (without actually blowing stuff up).

Also, there is this very nice video showing a domino chain reaction starting with a super, super tiny domino and leading to a 13th domino that is 1 meter tall.

Notice the claim that the 29th domino would be as tall as the Empire State Building (381 meters tall). Is that true? Let's look at the math of increasing sizes. Suppose we start with a domino that is 1 meter tall. How big will the next one be? It will be 1.5 multiplied by 1 meter to give a height of 1.5 meters. What about the next one? Again, we multiply by 1.5 to get a height of 2.25 meters. This phenomenon is called a geometric sequence, where each successive number in a list is found by multiplying by a constant.

Since multiplication can be tedious after a while, let's make a computer program to calculate the height of the 29th domino assuming the starting domino is 5 millimeters tall. Wait! Don't panic. Just because I said "computer program" doesn't mean this is going to be crazy complicated. Computers are our friends (for now), and you should learn to use them. Now is a great time to start.

Check out this code for a geometric series. You can click the Play button to run the code and then click the "pencil" to go back and edit the code. Yes, you can edit the code if you like (and you should).

From the output of this program you can see that the 29th domino would be more than 400 meters tall—even taller than the Empire State Building. Yup. Things can get pretty big fairly quickly with a geometric series. Let me go over the important parts of this very short program.

• In lines 4, 7, and 10 I am just creating some variables and assigning them to some value. The actually name doesn't matter, you could call them Bob, Jane, and Rhett and it would still work as long as you used these names consistently.
• The green lines that start with a number sign (3, 6, 9) are comments. They are just there for the humans. The computer ignores these.
• Line 12 is the most important. This is the start of a "while loop." Everything below this line that is indented will be repeated until the value of N is no longer less than 30.
• Line 14 and 15 are where the calculations happen. By adding 1 to N and setting it to N, the counter value increases. The other line calculates the new height.

Now for a test. See if you can modify the code to see how tall the 29th domino would be if the multiplicative factor was 1.4 instead of 1.5. What if you want to calculate the size of the 100th domino? Try it. If you change the code and run it—congratulations. You are now a computer programmer.

What about the dominoes in MythBusters Jr.? There are two major differences. First, they started with an actual domino. According to this site on dominoes, the standard domino is 1 and 7/8th inches tall (15/16" wide and 1/4" thick). The second difference is that all the measurements are in Imperial units (inches and pounds and stuff) instead of metric units. It's not a big deal, but I just like using metric units (like most of the world). OK, we will do it both ways.

Here is a similar program that calculates the height of dominoes needed to get one big enough to possibly crush a car. This code is a bit more complicated, but you can look at it if you like. From this, I get the following graph of domino height vs. domino number.

Here you can see that the 12th domino would have a height of about 4.12 meters. But what about the mass (and the weight—yes, weight is different than mass)? Let's assume that each domino has the same density, where density is defined as the mass per unit volume of a material. Since the 12th domino is 86 times taller than the original domino, wouldn't the mass also be 86 times greater? Nope. It's even more massive than that.

Check it. If I increase the height of a domino by a factor of 1.5, it does indeed get taller. However, if I want to keep the same shape for this bigger domino, I also have to increase the width by 1.5 and increase the thickness by 1.5. This means the volume of the new domino will increase by a factor of 1.5 x 1.5 x 1.5, or 3.375. If the density is constant, then the mass (and yes the weight) will increase by 3.375 for each successive domino.

Let's just plot this mass of the 12 dominoes. I am going to use metric—but don't worry, I will convert to pounds for you too.

That's over 3,200 kilograms for that last domino. If you convert that to pounds, it's more than 7,000 pounds. Bam.

But wait! If you're thinking this domino chain reaction is as simple as a few lines of Python code, you are forgetting something. Someone has to actually build these things. Oh sure, I could probably make the first 5 dominoes—but what about those last few? Can you just make them out of wood? That's possible, but it would have to be wood with the same density as the original domino. The other option is to build a metal frame and then fill the domino with stuff to make sure the mass is correct. But be careful, you also need to keep the center of mass in the center of the domino. It's a tough build—that's for sure.

OK, let me leave you with one homework question: How many chain-reaction dominoes would you need such that the last domino could fall over and crush an aircraft carrier? How much space would you need to make this happen? The good news is, I doubt anyone will ever build that domino.

For the Inuit of Labrador in Canada, climate disaster has already arrived. These indigenous people form an intense bond with their land, hunting for food and fur. “People like to go out on the land to feel good,” says Noah Nochasak in the documentary Lament for the Land. “If they can’t go out on the land, travel a long ways to feel good, they don’t feel like people.”

The Inuit’s lands, though, are warming twice as fast as the global average, imperiling the ice they rely on to travel. In the fall, hunters tend to get stuck in the community, because ice hasn’t fully formed up—and again, in the spring, when things are melting. Climate change is making these ice transition periods even longer.

“During those times historically, there has been some increases in suicide or suicide attempts or ideation in the communities,” says Ashlee Cunsolo, a health geographer who has studied the region. “There is a lot of concern among the mental health practitioners. What does that mean if this time is lengthened from two weeks to eight weeks?”

It’s known as ecological grief—the mourning of ecosystems and species and ways of life that are disappearing as the planet warms. But it isn’t just hitting the Inuit. As our planet plays host to rising seas, more intense storms, and higher temperatures, those conditions will support a growing international mental health crisis.

“Things like depression, anxiety, post traumatic stress disorder, substance abuse, domestic abuse, all these things tend to go up in the aftermath of a natural disasters,” says psychologist Susan Clayton of the College of Wooster, co-author of an extensive report on climate change and mental health. “As we have more natural disasters, one would expect to also have increases in those kinds of mental health consequences.”

Take Hurricane Katrina. In its aftermath, a sample of survivors found one in six met the criteria for PTSD. Rates of suicide and suicidal thoughts doubled. And especially in refugee situations, those mental health challenges can be intimately tied to physical health, compounding the harm. “When people are moving to places they bring diseases with them that the home population might not be immune to, and on the flip side these people are moving into places where they might not have immunity to the diseases in the new place,” says Jonathan Patz, director of the Global Health Institute at the University of Wisconsin.

Even those whose homes aren’t directly threatened by sea level rise or fiercer hurricanes aren’t immune. By the end of the century, the average American will have to endure four to eight times the number of 95+ degree days. Arizonans will get it particularly bad: Their number of 95+ degree days a year will leap from an average of 116 to over 200. And several studies have made a link between higher temperatures and higher rates of suicide.

One particularly data-intensive survey recently published in Nature Climate Change compiled temperatures and suicide statistics on the county level for the US, and municipality level for Mexico. They compared these granular regions not with each other, but with themselves—so the average monthly temperature in Palo Alto in July 2009 versus July 2010. This controlled for differences between locations in factors like poverty rates or gun ownership rates, both of which have been tied to suicide rates.

The uptick in suicide rates the researchers found may be small—a rise of 2 percent in Mexico and .7 percent in the US for every additional degree Celsius in average monthly temperature—and the relationship is far from simple. Rates of suicide fluctuate around the world, and where those suicide rates are highest, the temperature isn’t necessarily the highest. But extrapolated forward, the impact on public health could be devastating. “The fact that our results are so consistent across different socioeconomic strata, across different populations, suggests a common biological response,” says Stanford economist Marshall Burke, lead author of the study.

It's unclear if scientists will unearth shared mechanisms behind the mental health effects of climate-related trauma. But the experience itself is obviously, intuitively human. When Cunsolo and a colleague published an essay in Nature Climate Change earlier this year on ecological grief, the email response they got was huge, and it was cosmopolitan.

“It wasn't drought-affected farmers, it wasn't low-lying island states, it wasn't people who had been forced to relocate, it was people often living in urban settings would describe this overall sense of despair and anxiety,” says Cunsolo.

The root of our shared problem may be the same, but the manifestations of climate change can be wildly different. “Each region, each place, each culture, is going to experience something very, very different,” says Cunsolo. For the Inuit, it’s about ice. For the Southern US, it’s supercharged hurricanes. As with all health care, prevention is the best medicine. But in the case of climate change, we may be too late.

WIRED ICON

Marc Benioff, founder, chair, and co-CEO of Salesforce

NOMINATES

Boyan Slat, founder of the Ocean Cleanup

As a boy tinkerer in the Netherlands, Boyan Slat made zip lines and, at age 14, set a Guinness World Record for launching the most water rockets—213 of them—at once. You know, typical kid stuff. In hindsight, Slat says, “I just didn’t have a real problem to work on.” Soon, he found one. In 2012, at age 18, he gave a TedX talk outlining a tantalizing way to filter plastic waste out of the oceans’ gyres, vortices where sea-junk tends to accumulate. A few months later, Slat dropped out of engineering school, founded the nonprofit Ocean Cleanup, and began to design in earnest.

Slat’s goal was to build a system that uses ocean currents to push trash into a “passive collector,” which acts like an enormous lint trap, snaring everything from discarded fishing nets to scraps of plastic a few millimeters across. By the time you read this, the current prototype—a 600-meter-long, U-shaped floating tube suspending a stiff, 3-meter-deep screen­—should have already deployed to the Great Pacific Garbage Patch, a pair of swirling trash fields located between the US and Japan. If it works, then roughly every seven weeks, the trash will be taken out by boat and recycled.

Marc Benioff

First software business:
“At 14 I wrote a program called How to Juggle. I made $75!”

That matters because, well, the oceans are currently a mess. Marc Benioff, who contributed to the $22 million that the Ocean Cleanup raised last year, calls the problem of plastic pollution “out of control.” Noting that plastics have been in widespread use for only about 50 years, he adds, “Where are we going to be in another 50?” Already, one of the Ocean Cleanup’s first projects—a 30-boat trawl and airborne survey of the GPGP—concluded that the patch contains around 80,000 metric tons of plastic, far more than previously believed. Still, in five years, Slat estimates, an array of 60 such tubes could remove almost half of it.

Boyan Slat

Most inconvenient quality:
“I get seasick.”
—

Always carries:
A roll of duct tape.

Not everyone is so sanguine. Experts warned that one of Slat’s early designs, involving seabed anchors, wouldn’t work (the team scrapped it) and fretted that biofouling—the gradual accumulation of kelp and slime—would cause the tube to sink (unlikely, according to the company’s tests, though they’re looking into the possibility of adding a coating to keep sea-stuff from attaching to the screen). Others noted that Slat’s approach, targeting the top 3 meters of the gyre, would do little to address the problem of micro­plastic, the tiny fragments that now suffuse the seas and get eaten by fish—and then by us. (He counters that the gyre’s surface contains the most plastic by weight, and also that removing larger pieces will prevent them from degrading into trillions of additional pieces of microplastic.)

Eventually, Slat hopes to expand the project to all five ocean gyres, with the option for corporations and private groups to sponsor part of the cleanup array. For now, though, he is focused on the beta test. The Pacific Ocean is a notoriously rough environment, and parts of the Garbage Patch are more than a thousand nautical miles offshore. “The goal, first, is to prove the technology,” Slat explains. “We’ve really tried to eliminate every possible risk, but the only way to be absolutely sure is to do it.”

This article appears in the October issue. Subscribe now.

Update 9-18-2018, 1:40 pm EDT: This story has been revised to correctly describe Marc Benioff's contribution to the Ocean Cleanup.

MORE FROM WIRED@25: 1998-2003

• Editor's Letter: Tech has turned the world upside down. Who will shake up the next 25 years?
• Opening essay by Kevin Kelly: How the internet gave all of us superpowers
• Melinda Gates and Shivani Siroya: Giving (micro)credit
• Peter Thiel and Palmer Luckey: Remaking reality
• Sean Parker and Alex Marson: DNA is the next C++
• Jill Tarter and Margaret Turnbull: The E.T. hunters

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The birth of Lulu and Nana—the first two babies believed to be born with Crispr-edited DNA—has triggered soul-searching in China as tech innovators, scientific researchers, and government bureaucrats reconcile conflicting values.

At first Chinese media celebrated Jiankui He, the scientist who last week announced he had edited the girls' DNA. Some pundits even speculated whether a Nobel prize might be in the making. But within hours the story began to flip, and the narrative that emerged across the mainland was one of caution and censure. As Chinese scientists and technologists try to speed ahead with innovative research, they are also being reined in by government officials who are mindful of ethical sensibilities in China and abroad.

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The WIRED Guide to Crispr

If the news of the human embryo gene-editing experiment reached mainly science-minded readers in the US, in China its impact was far greater. On Weibo, a popular Chinese platform, 1.9 billion people viewed the hashtag “First Case of Gene-Edited HIV Immune Babies.” He’s research seemed to fit the script of the “China Dream,” a call for revolutionary scientific research and innovation issued by President Xi Jinping in 2017. This national policy aims to disrupt Western modernity with dreams of an Asian future.

Chinese scientists, however, did not jump to praise He. Some of them started issuing rebukes on social media. Zhengzhong Qu, a gene-editing scientist from China, criticized researchers who use “marketing gimmicks” to get famous, a reference to He's decision to post promotional videos of his work on YouTube. Qu also critiqued He's choice of DNA edits, which were done to confer built-in HIV resistance to the girls. “This has no meaning in clinical practice: there are already mature and effective techniques for protecting a baby from their parent’s HIV infections,” he posted on WeChat. “More risk than benefit in this case.”

Southern University of Science and Technology, where He is a professor, said it was “deeply shocked by this event” and launched an investigation. The government weighed in too: “China has banned reproductive use of gene editing in human embryos,” said Nanping Xu, the vice minister of science and technology.

To outsiders, the response to He's gene-edited babies may look like a departure from the speed-at-all-costs ethos that has seemed to characterize Chinese innovation in recent years. But a national rebranding campaign is underway. "Made in China," a label associated with cheap knock-offs, piracy, and stolen intellectual property, is being replaced with "Created in China." The gene-editing field is a prime example of how this dynamic is playing out.

Much of the action takes place in Shenzhen, the city where He works and which gave rise to the idea of "Shenzhen Speed." In the 1980s, skyscrapers were sprouting across the landscape faster there than anywhere else in the world.  Workers who aspired to better futures at iPhone factories flocked to Shenzhen from rural areas of China.

Immense speed comes with a cost. The city's own development has been hindered by an obsession with progress: In 2015, 17 buildings dramatically collapsed when a landslide of construction waste buried industrial buildings and workers’ living quarters. The risk of careless gene editing could be even greater.

Yet Shenzhen Speed has also been applied to the pace of biotechnology—the redesign of life itself. Chinese synthetic biologists have used Crispr to produce micro-pigs, humanized monkeys, and dogs with huge muscles. A Shenzhen company called BGI, which claims to be the largest genomics organization in the world, is a major player in the field of DNA sequencing. BGI has ambitions of sequencing every human and every form of life, and aspires to move "to writing, from design to synthesis."

But some of that bold language has been tempered in the wake of the Crispr babies scandal. "We need to be really careful not to do this kind of thing," says BGI’s Associate Director Xin Liu. Along with 53 other Chinese biotechnology companies, BGI issued a joint statement: "We must avoid the absolute pursuit of quick success in innovation and development, and rather work to strengthen industry self-discipline." These companies now say they aspire to "make life science and technology truly beneficial to mankind."

Gene-editing experiments on human embryos are continuing in China (and elsewhere)—but now, for research purposes only. At a summit on genome editing held in Hong Kong last week, Junjiu Huang, a biologist from Sun Yat-sen University, discussed how he had cloned human embryos and repaired a defective gene causing beta thalassemia, a blood disorder. But Huang concluded his talk with a strong condemnation of "any application of gene editing on human embryos for reproductive purposes. Such intervention is against the law, regulation, and medical ethics of China."

Huang himself sparked controversy in April 2015 when he used Crispr to create the world’s first gene-edited embryo. That incident triggered outrage internationally but got only a muted response in China. Secular Chinese ethics draws on Confucian thought, which assumes that a person becomes a person after birth, not before. So Huang was in the clear, but when He allowed edited embryos to then be born, he crossed an ethical line. Laws about gene editing were confusing in China before He’s experiment, but officials are quickly putting new rules in place. The government has now banned reproductive uses of Crispr, while saying that basic embryonic research will continue.

A bioethicist at the Chinese Academy of Social Sciences, Renzong Qiu, called on summit attendees to "protect the interest of the future child" and asked the Chinese government to develop "special regulations on applying genome editing in human reproduction." This would involve a licensing system and ethical guidelines to prevent eugenic uses of the technology.

Asian innovations in the field of biotechnology are redefining the horizons of possibility for the rest of the world. After the initial spark of fear in response to Huang's Crispr-edited embryos faded away, scientists in the United States and Europe ended up conducting similar studies.

Jennifer Doudna, the UC-Berkeley biochemist credited as one of the discoverers of Crispr, says that the current kerfuffle could play out in a similar way. "Two years from now, let’s say, if those girls are healthy…People will look back in retrospect and they will say, 'Maybe the process wasn’t correct, but the outcome could be fine.'"

Take a pencil, stretch out your arm, and let go. We all know that the pencil will fall. OK, but what about dropping a bowling ball? Is that the same thing? No wait! How about a watermelon dropped off a tall building? Why would you do that? I would do it to see it splat. Or maybe even more extreme, a human jumping out of an airplane. These examples could all be considered "falling," but not every fall is the same.

So let's get to this. Here is all the physics you need to know about falling things. Hold on to your seats. This is probably going to be more than you asked for. Don't worry, the math will (mostly) be at a simple level.

Falling without air resistance

I'm going to talk about air resistance down below. However, I want to start with the simplest case of an object falling near the surface of the Earth that has a negligible air resistance force. Really, this simplification isn't just approximately true in many cases, it's also one of the key components of the nature of science. If we want to build a scientific model (science is all about building models), the best bet is to start off with something without extra complications. If you want to model a mass on a spring, assume the spring is massless. If you want to model a cow, you have to assume it's a sphere (mandatory spherical cow joke). These simplifications are the first step to building more complicated models.

Is gravity constant?

This is one thing that comes up quite a bit. People say that if you drop two objects of different mass, they have the same gravity. OK, the first problem is the word "gravity"—what does that mean? It can mean many different things. The two most common meanings are: the gravitational force or the gravitational field.

Let's start with the gravitational field. This is a measure of the gravitational effect due to an object with mass. Since the gravitational interaction is force between two masses, you can think of this as "half" of that interaction (with just one mass). If you have an object near the surface of the Earth, then that object will have a gravitational interaction depending on the Earth's gravitational field. Near the surface of the Earth, the gravitational field is represented by the symbol g and has a value of about 9.8 newtons per kilogram.

No. The value of g is not the acceleration due to gravity. Yes, it is true that 9.8 n/kg has the equivalent units of meters per second squared. It is also true that a free falling (no air resistance) object falls with an acceleration of 9.8 m/s²—but it's still just the gravitational field. It doesn't matter what object you put near the surface of the Earth, the gravitational field due to the Earth is constant and pointing towards the center of the Earth. Note: It's not actually constant. More on that below.

What about the gravitational force? Here is a picture of two objects with different mass.

If you hold these two objects up, it should be clear that the gravitational force pulling down is not the same. The big rock has a bigger mass and a bigger gravitational force. That small metal ball has a much, MUCH smaller mass and also a much smaller gravitational force.

Yes, the gravitational force is also called the weight—those are the same things. But the mass is not the same as weight. Mass is a measure of how much "stuff" is in an object and weight is the gravitational force. Now to connect it all together. Here is the relationship between mass, weight, and gravitational field:

Technically, this should be a vector equation—but I'm trying to keep it simple. However, you can see that since g is constant, an increase in mass increases the weight.

Force and acceleration

OK, so you drop an object with mass. Once you let go, there is only one force acting on it—the gravitational force. What happens to an object with a force acting on it? The answer is that it accelerates. Oh, I know what you are thinking. You want to say that "it just falls," and maybe it falls fairly fast. That isn't completely wrong—but if you were to measure it carefully, you would see that it actually accelerates. That means that the objects downward speed increases with time.

Let's forget about falling objects for a moment. What about a small car on a horizontal, frictionless track with a fan pushing it? Like this:

If I turn on the fan and release the car, it accelerates. There are two ways I can change the acceleration of this car. I could increase the force from the fan or I could decrease the mass. With just a single force on an object in one dimension, I can write the following relationship.

This is what a force (or a net force) does to an object—it makes it accelerate. Please don't say forces make objects move. "Move" is a four letter word (that means it's bad). Saying an object "moves" isn't wrong, but it doesn't really give enough of a description. Let's just stick with saying the object accelerates.

There are many, many more things that could be said about force and motion, but this is enough for now.

Why do objects fall at the same time?

Now we can put together a bunch of stuff to explain falling objects. If you drop a bowling ball and a basketball from the same height, they will hit the ground at the same time. Oh, just in case you don't have ball experience—the bowling ball is MUCH more massive than the basketball.

Maybe they hit the ground at the same time because they have the same gravitational force on them? Nope. First, they can't have the same gravitational force because they have different masses (see above). Second, let's assume that these two balls have the same force. With the same force, the less massive one will have a greater acceleration based on the force-motion model above.

Here, you can see this with two fan carts. The closer one has a greater mass, but the forces from the fans are the same. In the end, the less massive one wins.

No, the two objects with different mass hit the ground at the same time because they have different forces. If we put together the definition of the gravitational force (on the surface of the Earth) and the force-motion model, we get this:

Since both the acceleration AND the gravitational force depend on the mass, the mass cancels. Objects fall with the same acceleration—if and only if the gravitational force is the only force.

But does the gravitational force decrease with height?

Yes. The gravitational field is not constant. I lied. Your textbook lied. We lied to protect you. We aren't bad. But now I think you can handle the truth.

The gravitational force is an interaction between two objects with mass. For a falling ball, the two objects with mass are the Earth and the ball. The strength of this gravitational force is proportional to the product of the two masses, but inversely proportional to the square of the distance between the objects. As a scalar equation, it looks like this.

A couple of important things to point out (since you can handle the truth now). The G is the universal gravitational constant. It's value is super tiny, so we don't really notice the gravitational interaction between everyday objects. The other thing to look at is the r in the denominator. This is the distance between the centers of the two objects. Since the Earth is mostly spherically uniform in density, the r for an object near the surface of the Earth will be equal to the radius of the Earth, with a value of 6,371 kilometers (huge).

So, what happens if you move 1 km above the surface of the Earth? The r" goes from 6,371 km to 6,732 km—not a big change. Even if you go ALL the way up to the altitude of the International Space Station orbit (400 km), there isn't a crazy huge change. Here, I will show you with this plot of gravitational field vs. height above the surface. Oh, and here is the python code I used to make this—just in case you want it.

For just about all "dropping object" situations, we can just assume the gravitational force is constant.

But what about air resistance?

OK, now we are getting into the fun stuff. What if you drop an object and you can't ignore the air resistance? Then we have a more complicated problem, because there are now TWO forces on the falling object. There is the gravitational force (see all the stuff above), and there is also an air resistance force. As an object moves through the air, there is a force pushing in the opposite direction of motion. This force depends on:

• The object's speed.
• The size of the object.
• The shape of the object.
• The density of the air.

The part that makes this complicated is the dependency of the air resistance on the speed of the object. Let's consider a falling object with significant air resistance. How about a ping-pong ball? When I let go of this ball, it is not moving. This means there is zero air resistance force and only the downward gravitational force. This force causes the ball to increase in speed (in the downward direction)—but once the ball is moving, there is now air resistance force pushing up. This makes the net force a little bit smaller, and thus you get a slighter increase in speed. Eventually the air drag and gravitational force have equal magnitudes. The ball then falls at a constant speed—this is called terminal velocity.

Since the net force on a falling object with air resistance isn't constant, this is a pretty tough problem. Really, the only practical (OK, not really the only way) to model this is with a numerical calculation that breaks the motion into tiny steps during which the force is approximately constant.

How about a model of a falling ping-pong ball? Here you go. Click the pencil icon to see and edit the code, and click Play to run it.

You can see that the ping-pong ball almost reaches a constant speed after dropping a distance of 10 meters. I put a "no air" object in there for reference. If you want to see what happens if you change the mass—go ahead and change the code and re-run it. It's fun.

Do heavier objects fall faster?

Now we get to the interesting question. If I drop two objects from the same height, does the heavier one hit the ground first? The answer is "sometimes." Let's look at three examples.

Drop 1: A basketball and bowling ball. Here is a slow-motion view of this actual thing.

If you ignore air resistance, then these two objects have the same acceleration, because they have different masses (see above). But why can you ignore the air resistance in this case? Looking at the basketball, it has a significant mass and size. However, it is moving fairly slowly during the fall. Even at the fastest part of this drop the force from air on the ball is super tiny compared to the gravitational force. Now, if you dropped it from a much higher starting point, the ball would be able to get up to a speed where the air drag makes it fall slower than the bowling ball.

Drop 2: A small ball and a cardboard box top. Just to be clear, the mass of the cardboard is WAY higher than the ball. Here is the drop. Sorry, the ball is hard to see since it's small.

Does the more massive object fall faster? Nope. In fact it's the lower mass that hits the ground first. It's not just mass that matters; size matters too. Even though the cardboard has a greater mass, it's surface area is also GIANT. This produces a significant air resistance force to make it hit the ground later.

Drop 3: Two pieces of paper. Two sheets of paper are pretty much the same, so they should have the same mass. However, they can hit the ground at different times.

I tricked you. Both papers have the same mass, but I crumpled one up, so they have different surface areas. The crumpled-up paper hits the ground first. It seems like this could be a good party trick. But again, it's about more than just the mass of the object.

What about different-sized skydivers?

Two people jump out of an airplane (with parachutes, because they aren't crazy). One person is large, and one person is small. Which one falls with the greatest terminal velocity? Yes, you can assume they are both in standard free fall position (same shape).

I am going to invoke the "spherical cow" principle and look at two falling spherical humans instead. Human 1 is a sphere with a radius of 1 meter (yes, that would be huge), and human 2 has a radius twice as big, at 2 meters.

How do the gravitational forces on these two spherical humans compare? Human 2 is obviously heavier. If the human density is constant, then the increase in gravitational force will be proportional to the increase in volume. If you double the radius of a sphere, you increase the volume by a factor of eight (volume is proportional to radius cubed). So human 2 has a weight eight times that of human 1.

What about the air resistance on these two humans? Again, human 2 will have a bigger area and more air resistance. If you double the radius, the cross-sectional area will be four times as much (since area is proportional to radius squared). Now you see that the bigger human will have a greater terminal velocity. Human 2 has a weight that is eight times as much, but air drag that is only four times as much as the smaller human.

Now let's take this to the extreme. An ant and an elephant jump out of a plane. The elephant is going to need a massive sized parachute, but the ant probably doesn't need anything. Since the weight-to-area ratio is super tiny for a super tiny object, the ant will have a very small terminal velocity. It can probably impact the ground with little injury. Note to my ant readers: Please be safe and don't try this in real life, in the unlikely event that I am wrong.

But size matters—especially when falling with air resistance.

I think this might be my longest blog post. Congratulations if you made it all the way to the end.

OK, I'm a little excited for the new Aquaman movie. Sure, I've been let down by DC movies before—but we also got Wonder Woman (which was awesome). Also, as a kid my mom made an Aquaman costume for me. She said it was the best costume for me since I had blonde hair (and so did Aquaman). But the real reason was that Aquaman didn't wear a mask—and masks are difficult to make. It was a great costume, thanks mom.

Now for the part where I do what I do—use physics to analyze a movie trailer. Let's get to it.

Although I don't really know what is going on, I know there is a submarine. I also know that Aquaman shoots out of the water and lands on this submarine. It is this scene that I will analyze.

Aquaman might be able to swim super fast, but once he leaves the water and enters the air there is only one force acting on him—the gravitational force that pulls straight down. Since the strength of the gravitational force depends on the mass of Aquaman AND the net force is equal to the product of mass and acceleration, the acceleration has to be equal to a constant 9.8 meters per second squared (the value of the local gravitational field).

Once in the air, Aquaman has the same acceleration as a rock that is tossed up. In the air, it's not about Aquaman, it's just about physics. Since it's physics, if I can look at his vertical motion I should be able to figure stuff out. In this situation, I can use video analysis to find his position in each frame of the video. This will give both position and time data so that I can plot his trajectory. Oh, but it's not exactly straightforward. In this scene, the camera (or virtual camera) seems to move forward. This means that the ratio of pixel size in the video to actual size will change with the position of the camera. I can compensate for this changing camera, but it takes some extra steps. If you want to do something like this yourself, check out Tracker Video Analysis. Very useful.

Here's what the motion would look like from a stationary camera.

Now for the physics. With a situation like this, there are actually three things to consider: the distance scale, the time scale (frame rate), and the vertical acceleration. In video analysis, you can pick two of these things to be known and then solve for the other one. In this case, I am going to assume the size of Aquaman and that the video plays in real time (so the frame rate is correct). Then I can plot the vertical position of Aquaman as a function of time. The plot should be a parabola. Here's what I get.

Yup. That looks like a parabola—so that's good. Even better, by fitting an equation to this data I can get a value for the vertical acceleration. It's twice the coefficient for the t² term. That puts that acceleration at 11.8 meters per second squared. On the surface of the Earth, a free falling object would have an acceleration of 9.8 m/s². Actually, these two values are really close—especially since I guessed the size of Aquaman in order to set the distance scale.

Why is this impressive? Let me first point out that this is most certainly a CGI scene. I doubt they got a stunt man to shoot out of the water and land on a submarine (but I've been wrong before). This means that they didn't just animate the motion of a digital Aquaman, they calculated his motion using physics. I think that's awesome.

But wait! There's more. Now that I have a trajectory for Aquaman, I can answer two questions. First, how high did he move out of the water? That's pretty easy. I can just look at the position vs. time graph and see that his change in vertical height was about 3.6 meters (almost 12 feet for Imperials). Second, how fast was he swimming in the water before he moved into the air? This is a fairly straightforward projectile motion problem. If you know the acceleration (and I do) and you know the maximum height (and I do), you can calculate the starting velocity. I'll leave the details as a homework assignment, but the answer is 8.4 m/s or about 19 mph (again, for Imperial unit users). That's pretty fast, but not the fastest fish in the ocean. The sailfish can get up to speeds of 30 m/s.

Of course, Aquaman might not be going full speed here. Why would he? He's just jumping on a submarine.