New Scientist has an excellent special report on nuclear power. Topics covered include how well more recently built reactors would have fared in the tsunami (many of them would have done just fine), the prospects for safe Thorium based reactors, and this graphic below on the relative risk of various types of energy. Nuclear, as you can see, has the lowest rate of deaths per unit energy produced over its lifetime:
The Economist has an article on aircraft designs that could cut fuel use by 50-70%, while still working with today’s airports (a challenge for flying wing designs). From the article:
Two groups working on the future of aircraft have come up with designs that could meet the practical needs of the industry and still cut fuel consumption by half. These researchers, at the Massachusetts Institute of Technology (MIT) and Imperial College, London, rely largely on existing technologies for many of their designs.
If a B737-800 was morphed into the shape of one of the D-series of aircraft on which Mark Drela is experimenting in MIT’s wind tunnel, then it would be about the same size, could fly the same routes and would carry a similar number of passengers. But the D8.1 version (which could be built conventionally, from aluminium) would use 49% less fuel. The D8.5 (similar, but constructed from composite materials expected to be available by 2035) would burn 71% less.
Michell Zappa has a fascinating infographic attempting to lay out timelines for future technologies over the next 25 years. It’s an impressive job of collecting data and laying it out in a way that someone can explore. It’s worth playing with. Click through on the link and you can zoom in and drag the graphic around to see what he’s projected, based upon predictions he’s collected from a dozen or so thinkers.
I do wish the infographic were more of a starting point for exploration. I want to click on some of the circles depicting future technologies and see what he’s using as a basis for the projection.
There are some things missing from the graphic as well, and some things on there that I think are implausible.
In general, when we think about what’s going to come down the pipe in technology, it behooves us to think about economics. What are the costs and cost trends of various technologies (either in R&D or development) vs. the demand for them or their economic return?
For this reason I think his projections about space (a lunar outpost and a space elevator both around 2030, for instance) are either implausible or will happen at a small scale. True, NASA has announced plans for a lunar outpost, but it appears to be backing away from them. Such an endeavor would, after all, be incredibly expensive, and offers little in the way of economic return. A space elevator, on the other hand, would lower the cost of access to space, but its guestimated $1 Trillion cost puts it out of the range of capital outlays any country or set of countries will consider for the coming decades.
On the other hand, I think Zappa under represents the impact of biotechnology and energy efforts over the coming years. An aging population creates a nearly insatiable consumer demand for new and better medical treatments. A growing and increasingly affluent population creates a tremendous demand for more agricultural output (especially as people move increasingly to eating meat, which requires far more land per calorie) and for more energy. The combination of apparently stagnating worldwide oil output and the eventual realization by most of the planet that we need to tackle climate change will force us to make the increasing energy supply a greener one.
Zappa covers Green Energy in the chart a bit, but it doesn’t quite convey that solar photovoltaic electricity, for example, will likely drop below the cost of coal electricity by 2020 (if not earlier), or the likely importance of biofuels created by genetically engineered organisms as ‘drop-in’ replacements for gasoline and kerosene. These are both quite near term impacts that will have a larger impact on the planet than space technologies, robotics, or artificial intelligence.
Zappa has a “Biotech” node which includes both a bit of medicine and a bit of food, but again I would have loved to see a more quantitative approach. Between now and 2050, population will rise by 35% and food demand will rise by 70-100%. Arable land, on the other hand, will not increase. One of the prime applications of biotech (in a broad sense) will have to be the increase of food yields per acre to meet that increased demand. Similarly, as the population ages, there will be more and more demand for therapies against the indignities of age. Stem cell treatments are a fantastic advance, but the major killers will remain heart disease and cancer. And the elderly will pump more and more dollars into products that allow them to age more gracefully, helping them look, act, and feel younger.
I very much applaud Zappa for attempting to lay out such a broad view of the future in a single place. It’s a challenging task that required synthesis of information from a number of sources.
Some things, though, are best looked at quantitatively. I would suggest we all think of the future trajectories of technology as being driven by the intersection of consumer demand (how much money are consumers willing to pump into something) and underlying cost trajectories (is the cost of something dropping or rising, and if so how fast?).
In that vein, here are what I see as the biggest trends going forward (with apologies for placing this only in text).
GROWING CONSUMER DEMANDS
1. Health & Longevity:
Worldwide healthcare spending is almost $4 Trillion, or around 10% of the GDP of developed countries, and is growing faster than any other category. Today there are less than 1 Billion people on the planet aged 60 or over. By 2050 there will be 2 Billion.
That doubling of elderly populations in OECD countries and China will place increasing demand for therapies that either slow aging (best case and not guaranteed) or which address the illnesses and loss of function, vitality, and appearance with aging. All up, healthcare will likely more than quadruple in total spending and double as a fraction of worldwide GDP by 2050, to more than 25% of the economies of developed countries and as high as 40% in some.
For instance, the US Council of Economic Advisors predicts that, on current trend, by 2040, healthcare spending will consume 35% of the economy of the United States.
That projected level of spending is more or less untenable. The aging population and rising health care costs will create enormous pressures for new technologies that can address medical needs at lower costs. That is one almost certain prediction.
Worldwide food spending is around $3 Trillion. While worldwide population will increase only by 35% by 2050, growing affluence will lead to a growing demand for less efficient meat and dairy foods.
As a result, total demand for grain (which is needed in large quantities to produce meat) is expected to nearly double by 2050.
At the same time, there is virtually no additional arable land to expand farming into. For farmers to keep up with demand, yields per acre will need to nearly double in the next few decades. At the same time, overpumping of aquifers, debates about pesticides and GMOs, and the energy inputs required in modern agriculture all serve as brakes on productivity gains.
3. Energy & Climate:
Worldwide energy demand is at $4 Trillion today and will roughly double by 2050. At the same time, populations and governments will come around to the need to virtually reduce net carbon emissions. That will place tremendous demand on both low-carbon energy sources and technologies to capture and sequester CO2 and other greenhouse gasses from the atmosphere.
Here is the US Energy Information Administration’s projections for world energy consumption through 2035.
4. Information Technology:
Information technology (including telecoms, computing hardware, software, and online services) is over $3 Trillion today and is growing at roughly 6% per year. Because it is so useful in so many different arenas of life, demand for it will continue to grow. While demand growth will likely slow over time, it could easily be three times as large in 2050, growing past all sectors except Health Care (which will almost certainly be the largest economic sector on the planet by a healthy margin).
Weighed against these consumer demands we have the underlying price and productivity trends.
1. Moore’s Law
Moore’s Law and its analogues for storage and bandwidth will, if they remain on their current paths, reduce the cost of a unit of computation, data storage, and data transmission by an estimated 100 million times by 2050. It remains to be seen whether these trends actually continue. Both physical challenges and potential saturation of consumer demand loom in the decades ahead. If they do continue, for many current applications, storage, bandwidth, and computation will be effectively free. (There will be exceptions for truly massive scale problems in physics, chemistry, biology, neuroscience, and artificial intelligence, where systems are incredibly complex and problems often scale extremely sub-linearly. These areas may be the prime economic drivers of continued improvement of IT power / $$ by mid century.)
2. The Dropping Cost of Genetic Information Processing
..will have a profound effect on biotechnology and medicine. We are much farther from personalized medicine than Zappa’s graphic would lead one to believe. But the Moore’s Law-like exponential drop in the cost of gene sequencing and gene printing will reduce the cost of sequencing a whole human genome to $5 by 2020 and pennies in 2030. In fact, the price of sequencing genes and of printing gene sequences has been dropping far faster than Moore’s Law:
The resulting flood of data, combined with the continued exponential rise in computing power, will start to make possible the large scale data mining necessary to truly extract valuable medical insights from the genome. Cheaper gene printing, cheaper proteomics, and cheaper experimentation systems based on similar trends will start to make an impact on delivering therapeutics, and also in turning manufacturing via synthetic biology into reality.
3. The Exponential Drop in Green Energy Price/Performance and Density
…if it continues, will herald a green energy revolution. Humanity’s energy use, from all sources, is roughly 1 / 6000th the amount of energy that the sun delivers to the planet. It is a huge and largely untapped resource with practically no greenhouse gas emissions. Solar power up until now has been uneconomical due to low efficiencies and high manufacturing costs of solar technologies. But over the last 30 years, solar photovoltaic cells have increased in energy returned per dollar of manufacturing cost by around 7.5% per year. On current pace, they will cross the price of coal-powered electricity between 2015 and 2020, and be half the price of coal around 2025-2030.
Solar does not solve all problems, of course. Intermittent power supplies (due to nights and cloudy days) make it an imperfect solution, but advances in energy storage will allow solar stations or home solar systems to store up energy during sunny periods and return it during the rest. By the 2030s solar should be fully competitive with coal (the cheapest fossil fuel energy source) for most of the world.
The predictions above could be wrong, of course. The world is full of surprises. Trends in consumer demand sometimes change. Exponential trends in technology are even more suspect and more likely to eventually flatten out. But whether these projections are right or wrong, if we want to have a rigorous look at the future, we should attempt to do so quantitatively, putting together our best data on what people want, on what’s possible, and on the trajectories of both.
As for how to sum this up in a single wonderful graphic, I leave that to someone else today. Perhaps Michell Zappa will take a shot. 🙂
New Scientist has an interesting article on research into what persuades people on scientific issues. The key finding is that there’s a major impact of hearing the evidence from someone who has similar political and social outlooks. Experts who are similar to listeners are inherently more believable.
The researchers tested this with a debate over giving HPV vaccines to school girls. Note that switching who provided the evidence affected the beliefs of both groups. Those who agreed politically with the new ‘expert’ saw their levels of agreement rise. Those who disagreed saw their levels of agreement drop.
The implication here is that, for skeptics on climate to be convinced, they need to hear the evidence from those who they politically agree with.
And as a 2010 Gallup Poll showed, the skeptics on Climate Change are by and large Republicans:
That means Republicans who believe in climate change are the ones who have the greatest chance of lifting nationwide belief that it’s a serious problem. Experts on the left remain vital as well, of course. But we are near the point where a majority of Democrats believe climate change is a serious problem. We are far from that point among Independents and farther among Republicans.
From the New Scientist article on political agreement with experts and how it affects their persuasiveness:
Yet people’s views do change if the right person is offering the evidence. Kahan investigated attitudes for and against giving the human papillomavirus (HPV) vaccine to schoolgirls to prevent cervical cancer – another divisive issue. After he presented people with both sides of the argument, he found that 70 per cent of egalitarian-communitarians thought it was safe, compared with 56 per cent of hierarchical-individualists.
When the “pro” argument was presented as coming from an expert painted as being in the egalitarian-communitarian camp, and the “anti” view came from a hierarchical-individualist, the split widened to 71 versus 47 per cent. But strikingly, swapping the experts around caused a big shift: 61 per cent of hierarchical-individualists then rated the vaccine as safe, compared to 58 per cent of egalitarian-communitarians. In short, evidence from someone you identify with sways your view.
[This is an update of a post I first wrote in March of 2011, responding to criticism of the analogy of Moore’s Law for solar power. Updating in April 2015, on the 50th Anniversary of Moore’s Law, in light of renewed conversation on this topic.
tl;dr: Moore’s Law is an analogy. As an analogy, it works. And progress is happening far faster than I projected in my ‘solar Moore’s Law’ piece of 2011.]
In March of 2011, as I was researching the book that would become The Infinite Resource, I plotted out the price of solar modules and found an exponential decline. Researching this, I found that nearly every other observer that had plotted the data had found the same. I wrote a guest blog post for Scientific American titled Smaller, Faster, Cheaper: Does Moore’s Law Apply to Solar Cells?In that post, I projected the future cost of solar power if the cost trajectory of solar modules continued, and other costs shrank in the same proportion. And crucially I found that new solar would be cheaper than new coal electricity across most of the US by 2020, and in the sunniest parts of the US by 2015 or 2016.
Paul Krugman linked to this post in his Sunday column Here Comes the Sunsome months later. I wasn’t the first to observe the exponential decline in solar module costs or the first to analogize it to Moore’s Law. I just boosted signal, and getting picked up by Krugman boosted the signal even further. In retrospect, there’s much I’d change about the piece: Differentiating retail vs. wholesale prices, talking more about the need for storage, talking about whole system cost reductions, talking more about how subsidies function, talking more about peak-of-day prices vs baseload prices, and more. But overall, it stands the test of time fairly well.
Before returning to the original piece, I’d note that actual progress in solar power module prices has been dramatically faster than I projected.
In the 2011 piece, the graphs project that in 2015, solar modules would cost just under $2 / watt. We’d reach 50 cents per watt in module price around 2030.
We have 50 cent per watt solar module prices today. Solar module prices are 15 years ahead of where those (at the time, optimistic) projections in 2011 placed them.
Say what you will about the analogy of Solar Moore’s Law – the numerical projections of price that I presented in 2011 were too conservative. Price reduction has happened far faster than the historical norm.
One weakness of the original piece is the assumption that whole system cost would drop at the same pace as module price. Because modules have plunged in price so fast, whole system cost hasn’t quite kept pace (though it’s done surprisingly well, driving by market forces). Another weakness is that the price line to beat is really wholesale electricity prices, around 6-7 cents per kwh, not the 12 cents per kwh I presented in the SciAm.
Even so, the projection of grid parity in the sunniest parts of the United States by 2015 or 2016 appears to have been correct. UBS is informing clients that earlier this year, NextEra, a subsidiary of Xcel energy, submitted bids for new solar projects in New Mexico at a cost of 4.2 cents per KWh. Even after backing out the 30% solar Investment Tax Credit that may soon expire, that would be a cost of 6 cents per kwh, lower than EIA’s estimate of 6.6 cents per KWh for new natural gas. This bid has not yet hit the media. I’ll link to it when it does.
This is a fine thing to be skeptical about. As I mentioned in the original post, we shouldn’t expect exponential trends to continue for ever. Most run up against external limitations at some point and level out or reverse.
It’s also worth noting that the solar gains are far slower than gains in computing. Computing gains have been roughly 60% in circuit density per year, and more or less the same in the annual gain of computing per dollar. Solar gains are much more modest, at roughly 7.5% gain per year. While computing performance per dollar seems to double roughly every 18 months, solar power per dollar doubles every 9 years. [Update: The solar pace over the last 37 years is now 14% improvement for year, or a doubling in watts / $ every ~4 years.] Moore’s Law and the price performance improvement of solar are both exponential trends (at least so far), but they have different slopes. Moore’s Law is clearly faster.
That said, the comparison between the gains in solar watts / dollar and the Moore’s Law increase of transistors per area (or the later morphing of this to computations / dollar) is fairly apt.
In both cases, they’re driven by three factors:
1. Nearly insatiable consumer demand for more of the resource (computing and energy, respectively)
2. Industry expectations. Any company working on a new microprocessor has to expect that their competition is going to be improving at a rate around that dictated by Moore’s Law (roughly, a doubling of transistors on the same size chip every 18 month). That gives companies working on new microprocessors a goal post to aim for. If they don’t hit that post, they can expect to be behind their competition. Similar factors apply in memory, in storage, and in bandwidth, each of which have their industry-noted exponential trends.The same dynamics work in solar photovoltaic power. Solar PV manufacturers have observed the same trend discussed here. As I noted in the original article, the trend is now 31 years old. Whether or not it will continue for 31 more years, any PV manufacturer has to expect that it will continue for at least the next few. That gives PV manufacturers their own goal posts to shoot for. And the PV market is crowded. Wikipedia lists more than 50 notable solar PV manufacturers.
UPDATE: In energy storage, where another exponential trend in price reduction exists, industry expectation is also a clear factor. Talk to any energy storage company in the world. They’re all watching this trendline and aiming to beat it.
3. Progress Made by Reducing Materials Per Output. The final factor is the one that makes the gains physically possible. In both computing and in solar, the gains being made in performance per dollar are being made by reducing the amount of material required to achieve each unit of output. By etching thinner lines, the semiconductor industry crams more transistors onto the same amount of silicon. They’re using less silicon per transistor. The solar PV industry, similarly, is using less silicon per watt and less manufacturing energy per watt. Solar manufacturers are doing this by reducing the thickness of solar cells, reducing losses in manufacturing, using more efficient ovens,(slowly) increasing the efficiency of solar cells, and increasingly by looking at techniques that use materials other than silicon. For a look at how industry thinks about this, here is a graph of silicon per watt that Sun Power presented at the SEMICON West Conference in 2007. And here’s a chart of decreasing silicon wafer thicknesses out to 2012 from an article by researchers at Applied Materials Switzerland, specifically focused on reducing silicon grams / watt.
4. Update: Total Cost of Ownership Matters – If there’s one more similarity I’d add between IT and solar (and batteries), it’s this: It’s not just technology cost that matters. It’s the total cost. Corporate buyers of computers long ago realized that the purchase of a computer was only a fraction of what they paid. The majority of the cost is really in the installation, deployment, and management of those systems.
In a sense, solar is no different, and storage will eventually be no different. The cost of the technology is plunging. But the total cost of the system is now more than twice the cost of the technology. To continue the true downward trend in the cost of energy from solar (or solar + storage), the total cost, including deployment, ancillary hardware, and maintenance has to be continually brought down. Arguably, this is now more important than module costs.
The similarity of the three factors tells me that the analogy is an apt one. That does not guarantee that it will continue forever. We will eventually hit the limit of what can be physically done to reduce materials needed for solar cells. But that looks likely to happen significantly after solar PV becomes less expensive than building new coal or natural gas electricity in most of the world. That is, indeed, transformative.
My post on the Moore’s Law-like exponential gains in solar power per dollar went up at Scientific American yesterday. Reprinting here with permission.
The sun strikes every square meter of our planet with more than 1,360 watts of power. Half of that energy is absorbed by the atmosphere or reflected back into space. 700 watts of power, on average, reaches Earth’s surface. Summed across the half of the Earth that the sun is shining on, that is 89 petawatts of power. By comparison, all of human civilization uses around 15 terrawatts of power, or one six-thousandth as much. In 14 and a half seconds, the sun provides as much energy to Earth as humanity uses in a day.
The numbers are staggering and surprising. In 88 minutes, the sun provides 470 exajoules of energy, as much energy as humanity consumes in a year. In 112 hours – less than five days – it provides 36 zettajoules of energy – as much energy as is contained in all proven reserves of oil, coal, and natural gas on this planet.
If humanity could capture one tenth of one percent of the solar energy striking the earth – one part in one thousand – we would have access to six times as much energy as we consume in all forms today, with almost no greenhouse gas emissions. At the current rate of energy consumption increase – about 1 percent per year – we will not be using that much energy for another 180 years.
It’s small wonder, then, that scientists and entrepreneurs alike are investing in solar energy technologies to capture some of the abundant power around us. Yet solar power is still a miniscule fraction of all power generation capacity on the planet. There is at most 30 gigawatts of solar generating capacity deployed today, or about 0.2 percent of all energy production. Up until now, while solar energy has been abundant, the systems to capture it have been expensive and inefficient.
That is changing. Over the last 30 years, researchers have watched as the price of capturing solar energy has dropped exponentially. There’s now frequent talk of a “Moore’s law” in solar energy. In computing, Moore’s law dictates that the number of components that can be placed on a chip doubles every 18 months. More practically speaking, the amount of computing power you can buy for a dollar has roughly doubled every 18 months, for decades. That’s the reason that the phone in your pocket has thousands of times as much memory and ten times as much processing power as a famed Cray 1 supercomputer, while weighing ounces compared to the Cray’s 10,000 lb bulk, fitting in your pocket rather than a large room, and costing tens or hundreds of dollars rather than tens of millions.
If similar dynamics worked in solar power technology, then we would eventually have the solar equivalent of an iPhone – incredibly cheap, mass distributed energy technology that was many times more effective than the giant and centralized technologies it was born from.
So is there such a phenomenon? The National Renewable Energy Laboratory of the U.S. Department of Energy has watched solar photovoltaic price trends since 1980. They’ve seen the price per Watt of solar modules (not counting installation) drop from $22 dollars in 1980 down to under $3 today.
Is this really an exponential curve? And is it continuing to drop at the same rate, or is it leveling off in recent years? To know if a process is exponential, we plot it on a log scale.
And indeed, it follows a nearly straight line on a log scale. Some years the price changes more than others. Averaged over 30 years, the trend is for an annual 7 percent reduction in the dollars per watt of solar photovoltaic cells. While in the earlier part of this decade prices flattened for a few years, the sharp decline in 2009 made up for that and put the price reduction back on track. Data from 2010 (not included above) shows at least a 30 percent further price reduction, putting solar prices ahead of this trend.
If we look at this another way, in terms of the amount of power we can get for $100, we see a continual rise on a log scale.
What’s driving these changes? There are two factors. First, solar cell manufacturers are learning – much as computer chip manufacturers keep learning – how to reduce the cost to fabricate solar.
Second, the efficiency of solar cells – the fraction of the sun’s energy that strikes them that they capture – is continually improving. In the lab, researchers have achieved solar efficiencies of as high as 41 percent, an unheard of efficiency 30 years ago. Inexpensive thin-film methods have achieved laboratory efficiencies as high as 20 percent, still twice as high as most of the solar systems in deployment today.
What do these trends mean for the future? If the 7 percent decline in costs continues (and 2010 and 2011 both look likely to beat that number), then in 20 years the cost per watt of PV cells will be just over 50 cents.
Indications are that the projections above are actually too conservative. First Solar corporation has announced internal production costs (though not consumer prices) of 75 cents per watt, and expects to hit 50 cents per watt in production cost in 2016. If they hit their estimates, they’ll be beating the trend above by a considerable margin.
What does the continual reduction in solar price per watt mean for electricity prices and carbon emissions? Historically, the cost of PV modules (what we’ve been using above) is about half the total installed cost of systems. The rest of the cost is installation. Fortunately, installation costs have also dropped at a similar pace to module costs. If we look at the price of electricity from solar systems in the U.S. and scale it for reductions in module cost, we get this:
The cost of solar, in the average location in the U.S., will cross the current average retail electricity price of 12 cents per kilowatt hour in around 2020, or 9 years from now. In fact, given that retail electricity prices are currently rising by a few percent per year, prices will probably cross earlier, around 2018 for the country as a whole, and as early as 2015 for the sunniest parts of America.
10 years later, in 2030, solar electricity is likely to cost half what coal electricity does today. Solar capacity is being built out at an exponential pace already. When the prices become so much more favorable than those of alternate energy sources, that pace will only accelerate.
We should always be careful of extrapolating trends out, of course. Natural processes have limits. Phenomena that look exponential eventually level off or become linear at a certain point. Yet physicists and engineers in the solar world are optimistic about their roadmaps for the coming decade. The cheapest solar modules, not yet on the market, have manufacturing costs under $1 per watt, making them contenders – when they reach the market – for breaking the 12 cents per Kwh mark.
The exponential trend in solar watts per dollar has been going on for at least 31 years now. If it continues for another 8-10, which looks extremely likely, we’ll have a power source which is as cheap as coal for electricity, with virtually no carbon emissions. If it continues for 20 years, which is also well within the realm of scientific and technical possibility, then we’ll have a green power source which is half the price of coal for electricity.
Plant pathologist Steve Savage has analyzed the data from the USDA’s Organic Production Survey (the largest ever survey of organic farming in the United States) and finds that organic yields per acre are substantially lower than the yields of conventional crops.
By far the biggest negative environmental impact of farming comes from deforestation to clear new land for farms. Lower yields mean more land is necessary to produce the same amount of food, which should make organic food proponents rethink whether or not organics are good for the planet.
An excerpt from Savage’s analysis:
In the vast majority of cases national Organic average yields are moderately to substantially below those of the overall, national average.
Examples for row crops include Winter Wheat 60% of overall average, Corn 71%, Soybeans 66%, Spring Wheat 47% and Rice 59%
Examples for fruits include Grapes 51%, Apples 88%, Almonds 56%, Avocados 62%,Oranges 43%, Strawberries 58%
Examples in Vegetables include Tomatoes 63%, Potatoes 72%, Sweet Corn 79%,Celery 50% and Cabbage 43%