Sage Geosystems, a Houston-based developer of geothermal energy and storage technologies, has a Department of Defense contract to examine the feasibility of installing a geothermal power system for Ft. Bliss. The U.S. Army Climate Strategy has a target of reducing greenhouse gasses from its facilities by 50% by 2030 (with a net-zero goal by 2050) and installing a microgrid on every base by 2035 in order to enhance energy security. 

Harnessing Dry Rock Geothermal Energy

A unique aspect of Sage’s technology is that it strives to tap into heat and pressure in what are known as dry-rock formations. Most installed geothermal systems are based on hydrothermal formations, where the geology interacts with existing water near the surface to produce steam. While the latter are relatively easy to extract heat from to run turbines for electricity, they are also relatively rare and relegated to regions with volcanic activity. 

“There are 16 gigawatts of geothermal power generation around the world,” said Cindy Taff, CEO of Sage Geosystems. “And all of that is hydrothermal, which geologically is a rare occurrence. Our understanding of geology and drilling techniques are enabling us to realize a goal of deploying geothermal energy everywhere.”

Sage’s technique drills down into rock formations to a depth of 9,000 to 20,000 feet where the ambient temperature is between 218 and 485 degrees Fahrenheit. This heat exists essentially everywhere. Fluid pumped into well reservoirs is heated by the surrounding rock and is then extracted to run a Rankine cycle turbine that runs on lower temperatures. Even better, it can heat “supercritical” CO2 (which has been pressurized to a near-solid state) to drive a turbine, which Taff says is more efficient. Sage has designed and built and is now testing a supercritical CO2-cycle generator. 

In a production facility, about 18 wells are envisioned that would produce a steady output of 50MW, enough to power at least 10,000 homes. Not all the wells are active at the same time. The process of introducing fluid to be heated and extracted eventually cools the surrounding rock. However, this heats back up again over time due to the ambient geological conditions. Plant managers rotate active wells, rather like farmers rotate crops, to keep the facility productive. 

Utilizing Fossil-Fuel Industry Tech

Taff said the company makes use of “off the shelf” drilling and related equipment from the oil and gas industry to construct its geothermal wells and reservoir fields. It’s an interesting example of fossil fuel technology being used to create clean energy. 

“Our technology relies on geological analysis and modeling that also originated in the oil and gas drilling industry,” Taff said. 

If the economics prove promising, the Ft. Bliss feasibility study could evolve into a test facility. Sage performed a similar study at the U.S. Air Force’s Ellington Field base in south Texas two years ago and is now building a prototype geothermal plant there. The technology can also be applied to long-term energy storage by using the Earth’s heat to keep fluid reservoirs at usable temperatures, ready to be tapped. The company has a contract with the Electric Reliability Council of Texas to build a 3MW geothermal energy storage facility for deployment later this year. 

Earth Day 1970 brought people (mostly white, young, and liberal) together on college campuses and in school auditoriums. High schoolers biked through smog-choked Denver and picked up litter on the state capitol lawn. Walter Cronkite hosted a CBS News special at the end of the day in which he concluded, “As a demonstration, it was mixed—beyond expectations here, far below there.” 

He underestimated.

Earth Day rode on a growing movement for environmental reform. Congress had passed the Clean Air Act in 1963 and the National Environmental Policy Act in January 1970. But actions accelerated after Earth Day, including—in fairly short order—the creation of the Environmental Protection Agency; the passage of the Clean Water Act, National Forest Management Act, and Endangered Species Act; and the beginning of the process to phase out leaded gasoline.

Earth Day largely succeeded in its goals: Skies have cleared up considerably from where they were, even in places like LA (although it still has a long way to go). And urban sewers like New York’s East River are now prime waterfront property. What’s more, Earth Day was part of a global trend of eco reform. Paris’s Seine River will (probably) be a competition venue at this year’s Summer Olympics. EarthDay.org claims to be active in more than 190 countries. 

Earth Day’s 1990 Reboot

In my freshman year of college—two decades after the first Earth Day—I joined a coalition of students in Washington, D.C., who united with Hayes and other earlier-generation leaders to reboot the movement.

Some of the environmental threats were still obvious and in the popular zeitgeist. “I’ve gotten real concerned over what’s going to happen with all the garbage,” Andie MacDowell’s character tells her therapist in the 1989 movie Sex, Lies, and Videotape. I felt the same. I could easily smell Staten Island’s massive Fresh Kills Landfill when I visited my big sister in New York City in the early 1990s. (It’s since been buried and turned into a park.)

But climate change was largely invisible. You couldn’t see the floods, droughts, disappearing glaciers, or planetwide coral bleaching that were, at that point, mostly just predicted to happen. So public attention focused on the obvious—like all that garbage. Recycling bins appeared everywhere. My “earthy-crunchy” friends and I even dug through trash cans on campus to redirect glass, aluminum, and plastic to the proper containers.

Corporate America jumped on the new environmentalist wave—in its marketing. The chemical industry assured the public that plastic could be rejuvenated at scale—part of many proclamations by companies that were often little more than feel-good advertising. Three decades later, as little as 21% of all U.S. recyclables make it into the bin, according to new research from The Recycling Partnership. And almost all the plastic that does still ultimately ends up in landfills, according to Greenpeace, which concluded that recycling plastic at scale is essentially impossible.

Of course climate change was on activists’ minds decades ago: It was already the biggest concern for many in my circles. But it was a lot harder to communicate to the public. Earth Day got a second reboot in 2000 to focus more on this greatest of challenges.

Earth Day in the Climate Era

Today is the 54th anniversary of Earth Day. That’s not the kind of round number that usually triggers a retrospective. But this is the most significant Earth Day in decades—the first in which the planet has consistently passed the 1.5 C warming mark that portends a point of no return.

Climate change has met our expectations from all those years ago—in the worst way. And it’s no longer hidden. Unprecedented Canadian forest fires choked the east coast last year and are expected to this year. Torrential rains just doused the Arabian Peninsula, of all places. Half the world’s coral reefs will be subjected to bleaching ocean temperatures this year, and global greenhouse gas emissions are still rising. The list could go on for pages and pages.

But many of the most hopeful expectations have also come true—especially in green power. Since 1977, solar panel costs have dropped from $126 per watt to 26 cents—and solar is now recognized to be the cheapest form of energy in most cases. Wind power prices are actually dropping faster than experts had predicted just a few years ago. CO2 emissions per capita have fallen considerably in the U.S. and China.

The progress on electric vehicles is stunning. General Motors’s all-electric EV1 was quite sophisticated when it debuted in 1996, and even pretty respectable by today’s standards. It was notoriously expensive to develop and produce, however. GM got cold feet, shut down production, and in 2003 began a process of literally crushing nearly all the models as their leases expired. 

But the product’s demise inspired Martin Eberhard and Marc Tarpenning to found Tesla Motors in 2003. Electric vehicles are now the leaders in innovation and style. They may not be taking off as fast as boosters had hoped. But their growth rate is in line with how all new technology gradually ramps up. Sure, electric vehicles accounted for just 7.6% of the U.S. auto market in 2023, but that’s up from 5.9% in 2022, and it could top 30% by the end of the decade, according to Kelley Blue Book.

Changing Views

Hearts and minds have changed, too. Environmental values have progressed from the earthy-crunchy minority to the bulk of people today. A Pew Research Center survey (from 2021) found that the majority of every U.S. generation, from Boomers up, feels that addressing climate change should be a top priority. The percentages generally go higher as respondents’ age goes lower. And the partisan divide may be fading. About half of Millennials and adult Gen Zers who identify or lean Republican felt that addressing climate change needs to be a priority. (Another study shows that anxiety about the climate predominates among younger people across 10 countries, including the U.S.)

A Deloitte survey found that slight majorities of Millennials and working-age Gen Zers in 44 countries research a company’s environmental impact and policies before accepting a job (though far fewer would quit over what they learn).

And many corporations have progressed from greenwashing to serious sustainability commitments that might meet with approval, such as in material sourcing, waste reduction, green-energy purchasing, and more. Several of these efforts are led by people Worth has honored in the Worthy 100 and Groundbreaking Women, such as Vincent Eckert (Swiss Re), Alex Liftman (Bank of America), and Rachel Slaybaugh (DCVC).

Do all these positive technological and cultural developments bring hope that humanity will turn back from the brink? Yes. Are they a guarantee? No. 

Stay tuned.

“There are many challenges associated with a concept power plant, and the standoff distance was one of them,” said Ryan Ramsey, First Light’s chief operations officer. “This result has now given us a clear and simpler pathway to increasing the standoff distance in a power plant, which will be several meters.”

Commercial-scale fusion reactors based on this design will require electric guns positioned far enough away to survive the intense heat of the reactor chamber and still be accurate enough to hit the fuel target.

Kicking Off a Fusion Reaction

A number of companies are pursuing various approaches to achieving sustained fusion reactions as a means of generating electricity. First Light is among those focused now on the inertial containment process, where pressure from the bombardment energy helps achieve the conditions necessary for fusion. Other popular methods use electromagnetic fields to produce the needed pressure.

The U.S. Lawrence Livermore National Ignition Facility (NIF) now almost routinely announces headline-grabbing inertial containment advances. These are often touted as examples of how commercial fusion generation is just around the corner. In this context, “ignition” means that more power was generated from a fusion reaction than was used to create it.

According to First Fusion’s Ramsey, the ignition demonstration at the NIF in December 2022 was a watershed moment for inertial fusion. “NIF’s result has revolutionized the way we think of fusion and is one of the biggest milestones towards developing a commercial fusion source, because it proves that the core physics works,” he said.

A Simpler, Cheaper Ignition System

However, NIF’s mission does not necessarily include commercial fusion development, and its expansive and expensive laser-based system is not practical for sustained power generation. 

The trick is accomplishing ignition using more modest means that can be scaled into a commercial power generation setting. First Light has settled on an approach to ignition that uses lower-powered electric guns to create intense pressure instead of massive lasers that create intense heat, as at the NIF.

First Light’s system fires coin-shaped metal alloy projectiles at a special gel surrounding the hydrogen fuel. This gel transmits the inertial energy of impact into a pressure wave that compresses the hydrogen atoms, triggering fusion and a massive release of energy.

Ramsey characterized the design of the fuel-gel package as the company’s “secret sauce” that enables it to use projectiles that can be fired at much lower power levels than laser-powered, heat-based inertial containment systems require. Individual fuel packages may be dropped into a chamber and then hit by projectiles accelerated to hypersonic speeds. The projectiles are pulse-fired at targets, with each ignition event creating intense heat that turns water into steam that drives a turbine to generate electricity. 

Achieving Commercial Scale

First Light says its recent achievement demonstrates that its electric guns can be made accurate, powerful, and reliable enough to deliver projectiles on targets consistently in a power plant setting. At the same time, fusion is still a bit hazy as to when it will generate electricity for consumers. Estimates range from a decade to never. Consider First Fusion’s achievement: There is a long way to go from 10 cm to several meters. Also, the electric gun is only one part of the problem. 

“We have various streams progressing in parallel, too,” Ramsey said. “A major focus for us is to continually improve our amplifier technology that is wrapped around the fuel capsule.” He adds that the company will continue to design and develop a pilot power plant based on its projectile fusion approach. 

The important features of inertial containment remain in laboratory projects and simulations. At the same time, the radical improvement in the standoff range of the electric gun, one of its key components, shows that the way forward is promising.

While flying private does emit more carbon per person than riding coach or even first class, the overall scale of the pollution is relatively small. Aviation is responsible for 2% of global carbon emissions, according to the International Civil Aviation Organization, and private jets account for 2% of that 2%.

What’s more: The business aviation sector has been at the leading edge of efforts to decrease carbon emissions. And it offers various programs for flyers looking to reduce their environmental impacts.

Sustainable Aviation Innovations

Private aviation manufacturers pioneered innovations such as lighter composite materials and fuel-reducing winglets that have been adopted by the airline industry.

Embraer, which has manufactured the best-selling light jet for the past decade, its Phenom 300, is developing the Energia family of concept aircraft using renewable energy propulsion technology. Under development are hydrogen fuel cells, hybrid-electric airplanes, and dual-fuel engines that could, for instance, run on traditional jet fuel or hydrogen.

Bombardier, which makes the Global 7500, a private jet that can fly nonstop from Hong Kong to New York, manufactures both its long-range business jets and the super-midsize Challenger 3500 with an environmental product declaration. The verified EPD measures the impact from design to production and over the plane’s lifetime. That includes supply chains, down to the wood veneers that adorn the cabin finishings.

In December, Textron Aviation launched SustainableAdvantage with industry sustainability solutions provider 4Air to help aircraft owners reduce carbon emissions. It already uses renewable, zero-emission wind energy for manufacturing at its Kansas production facilities.

Carbon Offsets for Flights

Large and small private jet flight providers have been offering carbon offsets for over a decade—allowing flyers to invest in carbon-reduction projects to make up for the emissions from their flights. Stratos Jet Charters, an Orlando-based charter broker, first offered a carbon-offset option for clients when it launched in 2007.

According to buyer’s guide Private Jet Card Comparisons (of which I’m founder and editor-in-chief), more than three dozen fractional and jet card providers currently offer carbon offsets for clients.

Sentient Jet, which invented the jet card in 1999, includes in its price 300% offsets to account for warming emissions. Since 2021, it has offset 1,339,235 metric tons of CO2.

The offset efforts range from forestry projects in Massachusetts and Tennessee to installing efficient cookstoves in Malawi and funding renewable energy projects in India and Turkey.

Sustainable Aviation Fuel

Not all offset programs are as effective as claimed, and they aren’t enough on their own to reduce emissions. The private aircraft industry is undertaking deeper emission-cutting efforts.

VistaJet says it is on its way to being carbon neutral by 2025—25 years ahead of the industry’s goal. Its efforts include carbon offsets but also more efficient flight planning, eliminating onboard plastics, and cleaning up the vast supply chains it takes to operate one of the world’s largest private jet fleets.

Along with all the other providers, VistaJet is also promoting sustainable aviation fuel, or SAF, which can be made from municipal waste, used cooking oil, biomass or other sources in place of petroleum. SAF has the potential to reduce carbon emissions by up to 80%.

NetJets, the world’s largest private flight provider, has invested in a facility in the Philippines that will turn trash into SAF. Like most providers, it has focused beyond flying to ensure its corporate offices are carbon neutral.

SAF is still just a tiny fraction of the jet fuel market, accounting for less than 1%, and it can cost 3-5 times as much as traditional fuel. (Although no planes run on 100% SAF, but rather a blend with standard fuel.) But many private aviation companies are offering it as an option, helping to build demand and foster the industry.

Another challenge is that the places where SAF is available still need to be expanded. To counter that problem, Victor, a U.K.-based broker, now allows clients to choose SAF for each of their charter flights using a process called book and claim. That means even if SAF isn’t available for their flight, the sustainable fuel they purchased is used by another aircraft at a different location, thus reducing output. Jet Aviation, a unit of General Dynamics and a leading private jet management company and charter operator, does the same for its customers.

And those six-minute celebrity flights that create such a stir are what’s called “empty-leg repositioning.” After an airplane drops off its owner, it must then fly empty to an airport with available parking spots. There’s a shortage of spots, in part, due to protests against expanding airports.

Venture capital firms are partnering with software engineers to train AI-driven machine learning systems (AIML) to provide a competitive advantage in key industry sectors. The most promising of these is weather prediction. Moreover, hedge funds are seizing on these systems to identify weather-related risks to their portfolios better—be it predicting regional citrus harvests or managing energy generation sources for the electric grid. And achieving advantages over their competitors could be worth tens of billions of dollars.

Julie Pullen is a partner at Propeller Ventures, a VC firm specializing in businesses developing ocean and climate technologies. She is building teams to use AIML techniques to enable organizations to customize forecasts for their commercial or scientific needs. Such systems can use proprietary and unique data sources for focused and precise short- and long-term weather prediction not currently available from government sources. 

A research scientist at Columbia University’s Earth Institute and former head of the U.S. National Maritime Security Center, Pullen has stepped into the venture capital world to apply her meteorology, oceanography, and climate science knowledge to train AIML systems. 

“I see a lot of companies being formed around teams of data scientists to improve weather prediction with AI,” Pullen says, citing Google GraphCast, Huawei Pangu, and Nvidia FourCastNet. “There is a lot of competition entering the field. My team is working on achieving a deeper understanding of the Earth’s atmosphere and oceans for people across an array of disciplines and industries.”

Hedge funds are not only eager customers of weather information; their relative success in using it to improve their portfolios (or not) is an excellent way to validate AIML algorithms. 

Customizable Forecasts for Investors

Until recently, weather prediction was such a high-end computing problem that only government agencies, such as the U.S. National Weather Service or the European Center for Medium-Range Weather Forecasts, could pull it off. Moreover, weather forecasting requires many sensors—satellites, ground stations, aircraft, ocean buoys, and other sources—to inform the models. 

While forecasting has made great strides in recent decades, the scarcity of government service providers, reliable as they are, means everybody essentially has access to the same data. This is great for people planning their day, but it does not offer a competitive advantage to firms whose fortunes depend on atmospheric conditions. 

Weather is a valuable industry because everybody needs information about it at some level. With the primary data sources homogenized, the opportunity is to customize forecasts for specific users who then apply them to their activities. 

For example, IBM acquired its Weather Company business in 2015, integrating the company’s assets with its own data analysis, presentation, and Watson AI tools. In August, IBM signed a deal to sell The Weather Company to private equity firm Francisco Partners. However, IBM says it will continue providing software support and development. But acquisitions or partnerships like these have their limits: The sources of raw data are essentially the same as what everybody else gets—largely from government agencies.

Satellites, radar, terrestrial stations, and oceanographic sensors feed data into the great, government-supported forecasting service computing systems, which analyze them using some of the most complicated software systems on the planet. The atmosphere is a tremendous fluid, and few things are harder to model than a fluid. One of the areas for improvement of government weather service-supplied information is that the massive computing power needed to process complex models limits the number of forecasts to a few per day.

Trained AIML systems can make calculations based on pattern recognition processes much more quickly and as accurately, according to a study published in the journal Science. Another advantage is AIML’s ability to take in various unstructured and custom datasets, including text documents, photographs, audio, video, and other sources not in a machine-recognized format. The key is in the training: Specialists teach the AI to recognize patterns and readings from historic data sources, formatting unstructured data as needed. These may come from government weather services, scientific research, and even fossils and paleoceanography (e.g., core samples of arctic ice or deep ocean mud) from millions of years ago. 

Expanding Risk Assessment with AI

Julie Pullen says the widespread affordability of AIML platforms means that individual systems can be trainable in a wide range of specific tasks and open to an expanding range of sources to inform their analyses.

“Venture capital is actively making bets in that space,” Pullen says. “And one of the best ways of getting feedback on those bets is from the hedge fund industry when it uses those tools. It’s the intersection of the scientific with the financial that demonstrates value for predictions along a range of timescales.”

She points out that the trainable, customizable features of AIML enable hedge fund managers to look at very specific weather conditions with fine regional detail. Funds can better predict extreme weather events such as heat waves and droughts on time horizons ranging from weeks to months to years. These forecasts make a difference to the bets fund managers make. Then they can get rapid feedback on the models, looking at results from shorter time horizons. 

According to Harun Dogo, manager of quantitative analysis at Los Angeles-based hedge fund TCW, widely used weather models are informative. But they make positions in certain commodities expensive to hold because everybody comes to the same conclusion. These widely held positions also come with various levels of risk, not just because they are expensive but also because, at some point, unexpected events—weather-related or geopolitical—will intervene to undermine the common wisdom and destabilize markets. The trick is to be able to make bets against the prevailing wisdom in time and be right about them.

“Artificial intelligence, and really it’s machine learning, is a way to classify patterns in the market and use that information to optimize your positioning,” Dogo says. “What do you think the expected volatility will be? A machine learning algorithm can help you identify risks you didn’t even know you had, things that other quantitative analysts might have missed.”

For example, the world has transitioned from a La Niña weather cycle over the Pacific to an El Niño cycle that shifts global rainfall patterns. Funds dealing in soft commodities (e.g., cocoa futures), energy, and even mining will factor how these predicted patterns may affect portfolios incorporating these sectors. However, everybody has this information. Dogo says AILM offers the opportunity to look at very specific localities on shorter time horizons and discern patterns not covered by historic models of La Niña-El Niño periods.

“So really, the question is, what’s the new information that AIML-enabled climate or weather datasets add to your ability to think about where a particular asset pricing will go?” Dogo says. “How volatile [is it] going to be and what the potential downsides of holding it on particular time horizons will be.”

TCW works with third-party partners to receive AIML information to supplement its analysis activities. At this state of the technology’s development, it is not clear that having an in-house capability is worth the time or the resources. In January, Nvidia announced an agreement with datacenter operator Equinox to offer corporate clients AI and machine learning systems. The field is still in its infancy, and questions of whether AIML is better as a third-party service, or an in-house proprietary tool have yet to be answered.

Hedge fund Citadel reportedly made a significant investment in scientists and computer engineers to develop in-house forecasting tools for giving it competitive advantage for trading commodities, especially in the energy sector. Last year, the Financial Times reported that the weather team helped the company earn $16 billion in 2022 to become the most successful hedge fund. The team produces forecasts that are focused on conditions in regions where opportunities for trading in raw materials are most promising. 

Hedge Funds Mirror Scientific Researchers

The advent of AIML systems that can run on modest networks of desktop computers is expanding the scope of private weather prediction, opening a new professional track for meteorologists. The website eFinancialCareers.com says meteorologist is becoming a hot job opportunity at hedge funds, with top annual salaries north of $1 million.

From Pullen’s perspective, hedge funds are great as enablers of AIML systems development and validators of analyses and forecasts from such systems. Her team at Propeller is not specifically interested in hedge funds as clients: Its mission is nurturing technologies for improving ocean and climate conditions. However, Pullen is focused on the incredible potential of AILM systems for better understanding marine and weather dynamics on Earth. It just so happens that hedge funds value this information as well and offer an excellent proving ground for the technology.

While hedge funds are notoriously secretive organizations and are not prone to sharing their competitive advantages openly, their requirements for using unstructured data from a variety of sources mirror those of the scientific community. The convergence of science with better financial returns is a powerful dynamic for proving AIML algorithms. 

Gordon Gould at San Francisco-based hedge fund Numerai is developing an AIML model that takes information supplied by data scientists to predict the stock market. The fund pays out to subscribers based on how good their data is at enabling the algorithm to foresee market movements. The providers whose data make the most money get the best returns. This sort of on-the-fly data validation is invaluable for better training AIML systems. 

AI Opens Up New Business Understanding

The ability to incorporate and analyze unstructured data will be what separates AIML systems from each other. That’s where the competitive advantage lies. It is also how we will better understand our rapidly changing world.

The reliable, routine sources of data that make it into the government service forecasts and supply private weather services are not going to be enough for hedge funds and other users hungry for information from multiple sources in granular detail. Nor, Julie Pullen adds, will it be enough for the development of breakthrough AILM systems for better understanding the Earth and its atmosphere and oceans.

“It’s the datasets that don’t get incorporated into the top forecasts that you want,” she says.

Mainstream forecasts are not necessarily getting data sets acquired by, for example, scientists who might be going out on a research cruise, dropping sensors over the side of a ship, or releasing weather balloons off the deck. There are a lot of observations going on by commercial ships and aircraft just going about their routine business that could be valuable. Private companies are getting into the instrumentation business, including satellites.

Pullen says that part of the challenge in building standout AILM systems is identifying unique sources of data and then providing a pathway for that information to be incorporated into your model. Not only will this data arrive in a huge number of formats, but it may also not always be available from one forecast to the next. Customers want reliability as well as uniqueness.

“The data can be a little wacky,” she says. “Sometimes it’s [something] buried in the deep mud of the Pacific that tells us just what the temperature was at a certain point in time. Sources can be wildly unstructured.”