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Study measures marijuana's carbon footprint—and it's high
Growing marijuana in large, climate controlled warehouses is good for production but has a massive carbon footprint.
- A new study finds that the kilo of marijuana can come with a carbon footprint of up to five tonnes.
- The exact value differs by state, with climate and the availability of clean energy being important factors.
- Alternatives to growing the plant in warehouses can drastically reduce emissions.
At the time of writing, marijuana is legal in 14 of the United States and decriminalized or permitted for medical purposes in 16 more. Several other territories have taken similar steps as well. After a long and costly drug war, the political and cultural momentum behind decriminalizing marijuana appears to be unstoppable.
However, these legal changes have ramifications for how the plant is tended. While past growing methods focused on balancing the need to keep the plant hidden with botanical concerns, modern techniques are increasingly focused on mastering industrial-scale production within legal limits. Indoor growing is a popular answer for both situations, keeping warrant-less prying eyes away in one case while also allowing for heightened security and climate control in another.
These operations cost a small fortune to maintain the needed apparatus. Since cultivators have come out of hiding and industrialized, the costs involved have only grown. Modern indoor facilities consider the temperature, humidity, and even the composition of the air and how they affect their plants — all of which call for equipment that eats electricity like it has the munchies.
Information on how much pollution these operations were creating has been lacking up until now. A new study published in Nature Sustainability measures the carbon cost of industrial marijuana production in every state and considers ways to make the green stuff a little more green.
The hippies finally got their legal weed at a high cost to the environment? How Faustian!
The study uses a model based on the actual operating procedures of a modern warehouse-style growing system, like the kind used by 41 percent of producers who sell in the legal market.
It accounts for factors like the warehouse's HVAC system, which replaces the air in the room an average of 30 times an hour, the air conditioning, the heating, the humidity control, the lighting, the cost of producing supplemental CO2 to aid plant growth, the costs of the average irrigation system, and other elements of production and distribution. Information for different locations can be plugged in, areas with climates unsuited for growing the plant will incur higher temperature control costs, and the required electricity be calculated.
This information can be compared to the known carbon cost per kilowatt-hour in a given area. The results of feeding different information into this model can be seen on this map:
The carbon price of producing marijuana in a modern warehouse by area in the 50 states and DC.
Credit: Jason Quinn et al.
As certain stereotypes would lead you to suspect, southern California can produce marijuana at the lowest environmental cost, caused both by a reduced need for climate control and the abundance of renewable energy in the local grid. The highest costs were incurred in Hawaii, partly due to the burning of oil to produce power on the islands and the large carbon footprint this creates. Differences across the country can be explained in similar terms, with some areas needing lots of carbon-intensive electricity to produce cannabis and others having cleaner energy or more suitable climates.
Across the country, the price of a kilogram of cannabis flowers, the part which is smoked, ranges from around two to five tonnes of carbon dioxide.
I spoke with several "experts" who agreed that the typical American joint has roughly .3 grams of marijuana in it. Using the above data, we can estimate that your regular smoke requires just over one kilo of greenhouse gases to produce, equivalent to burning an eighth of a gallon of gasoline. For comparison, a single bottle of beer might produce half that, and the footprint of an entire bottle of wine is only slightly higher.
What can be done about these emissions?
The authors point out that most of these environmental costs, perhaps 80 percent, are tied to the methods used to grow the plant indoors and can be reduced by making outdoor cultivation feasible. Such a shift would have noteworthy effects on a state's overall carbon footprint. As the study says:
"If indoor cannabis cultivation were to be fully converted to outdoor production, these preliminary estimates show that the state of Colorado, for example, would see a reduction of more than 1.3% in the state's annual [greenhouse gas] emissions."
Such a switch would reduce the carbon footprint of the plant's production by 96 percent. If the change were instead from warehouses into greenhouses, the cut would be a still substantial 43 percent, and the various benefits of growing the planet inside, such as security, would remain.
Additionally, large variations between indoor operations exist as well, some of which are not fully described in the above map. In Colorado, for example, the carbon cost of growing marijuana in Leadville is 19 percent higher than it is in Pueblo, primarily due to differences in climate. If state regulations allowed cannabis grown in Pueblo to be sold in Leadville, the net carbon emissions would fall even after accounting for transportation. The same might be said for interstate sales, though that seems further off.
In the heady rush to legalize marijuana, the question of how this would impact the environment seems to have slipped past state legislatures, producers, and consumers. General efforts to lower greenhouse gas emissions will have to take the production of a drug that 13 percent of American adults use each year into account.
- Marijuana addiction has risen in places where it's legal - Big Think ›
- Which is Worse? Alcohol or Marijuana? - Big Think ›
- Carl Sagan on why he liked smoking marijuana - Big Think ›
Inventions with revolutionary potential made by a mysterious aerospace engineer for the U.S. Navy come to light.
- U.S. Navy holds patents for enigmatic inventions by aerospace engineer Dr. Salvatore Pais.
- Pais came up with technology that can "engineer" reality, devising an ultrafast craft, a fusion reactor, and more.
- While mostly theoretical at this point, the inventions could transform energy, space, and military sectors.
The U.S. Navy controls patents for some futuristic and outlandish technologies, some of which, dubbed "the UFO patents," came to life recently. Of particular note are inventions by the somewhat mysterious Dr. Salvatore Cezar Pais, whose tech claims to be able to "engineer reality." His slate of highly-ambitious, borderline sci-fi designs meant for use by the U.S. government range from gravitational wave generators and compact fusion reactors to next-gen hybrid aerospace-underwater crafts with revolutionary propulsion systems, and beyond.
Of course, the existence of patents does not mean these technologies have actually been created, but there is evidence that some demonstrations of operability have been successfully carried out. As investigated and reported by The War Zone, a possible reason why some of the patents may have been taken on by the Navy is that the Chinese military may also be developing similar advanced gadgets.
Among Dr. Pais's patents are designs, approved in 2018, for an aerospace-underwater craft of incredible speed and maneuverability. This cone-shaped vehicle can potentially fly just as well anywhere it may be, whether air, water or space, without leaving any heat signatures. It can achieve this by creating a quantum vacuum around itself with a very dense polarized energy field. This vacuum would allow it to repel any molecule the craft comes in contact with, no matter the medium. Manipulating "quantum field fluctuations in the local vacuum energy state," would help reduce the craft's inertia. The polarized vacuum would dramatically decrease any elemental resistance and lead to "extreme speeds," claims the paper.
Not only that, if the vacuum-creating technology can be engineered, we'd also be able to "engineer the fabric of our reality at the most fundamental level," states the patent. This would lead to major advancements in aerospace propulsion and generating power. Not to mention other reality-changing outcomes that come to mind.
Among Pais's other patents are inventions that stem from similar thinking, outlining pieces of technology necessary to make his creations come to fruition. His paper presented in 2019, titled "Room Temperature Superconducting System for Use on a Hybrid Aerospace Undersea Craft," proposes a system that can achieve superconductivity at room temperatures. This would become "a highly disruptive technology, capable of a total paradigm change in Science and Technology," conveys Pais.
High frequency gravitational wave generator.
Credit: Dr. Salvatore Pais
Another invention devised by Pais is an electromagnetic field generator that could generate "an impenetrable defensive shield to sea and land as well as space-based military and civilian assets." This shield could protect from threats like anti-ship ballistic missiles, cruise missiles that evade radar, coronal mass ejections, military satellites, and even asteroids.
Dr. Pais's ideas center around the phenomenon he dubbed "The Pais Effect". He referred to it in his writings as the "controlled motion of electrically charged matter (from solid to plasma) via accelerated spin and/or accelerated vibration under rapid (yet smooth) acceleration-deceleration-acceleration transients." In less jargon-heavy terms, Pais claims to have figured out how to spin electromagnetic fields in order to contain a fusion reaction – an accomplishment that would lead to a tremendous change in power consumption and an abundance of energy.
According to his bio in a recently published paper on a new Plasma Compression Fusion Device, which could transform energy production, Dr. Pais is a mechanical and aerospace engineer working at the Naval Air Warfare Center Aircraft Division (NAWCAD), which is headquartered in Patuxent River, Maryland. Holding a Ph.D. from Case Western Reserve University in Cleveland, Ohio, Pais was a NASA Research Fellow and worked with Northrop Grumman Aerospace Systems. His current Department of Defense work involves his "advanced knowledge of theory, analysis, and modern experimental and computational methods in aerodynamics, along with an understanding of air-vehicle and missile design, especially in the domain of hypersonic power plant and vehicle design." He also has expert knowledge of electrooptics, emerging quantum technologies (laser power generation in particular), high-energy electromagnetic field generation, and the "breakthrough field of room temperature superconductivity, as related to advanced field propulsion."
Suffice it to say, with such a list of research credentials that would make Nikola Tesla proud, Dr. Pais seems well-positioned to carry out groundbreaking work.
A craft using an inertial mass reduction device.
Credit: Salvatore Pais
The patents won't necessarily lead to these technologies ever seeing the light of day. The research has its share of detractors and nonbelievers among other scientists, who think the amount of energy required for the fields described by Pais and his ideas on electromagnetic propulsions are well beyond the scope of current tech and are nearly impossible. Yet investigators at The War Zone found comments from Navy officials that indicate the inventions are being looked at seriously enough, and some tests are taking place.
If you'd like to read through Pais's patents yourself, check them out here.
Laser Augmented Turbojet Propulsion System
Credit: Dr. Salvatore Pais
- The history of AI shows boom periods (AI summers) followed by busts (AI winters).
- The cyclical nature of AI funding is due to hype and promises not fulfilling expectations.
- This time, we might enter something resembling an AI autumn rather than an AI winter, but fundamental questions remain if true AI is even possible.
The dream of building a machine that can think like a human stretches back to the origins of electronic computers. But ever since research into artificial intelligence (AI) began in earnest after World War II, the field has gone through a series of boom and bust cycles called "AI summers" and "AI winters."
Each cycle begins with optimistic claims that a fully, generally intelligent machine is just a decade or so away. Funding pours in and progress seems swift. Then, a decade or so later, progress stalls and funding dries up. Over the last ten years, we've clearly been in an AI summer as vast improvements in computing power and new techniques like deep learning have led to remarkable advances. But now, as we enter the third decade of the 21st century, some who follow AI feel the cold winds at their back leading them to ask, "Is Winter Coming?" If so, what went wrong this time?
How to build an A.I. brain that can conceive of itself | Joscha Bach | Big Think www.youtube.com
A brief history of AI
To see if the winds of winter are really coming for AI, it is useful to look at the field's history. The first real summer can be pegged to 1956 and the famous Dartmouth University Workshop where one of the field's pioneers, John McCarthy, coined the term "artificial intelligence." The conference was attended by scientists like Marvin Minsky and H. A. Simon, whose names would go on to become synonymous with the field. For those researchers, the task ahead was clear: capture the processes of human reasoning through the manipulation of symbolic systems (i.e., computer programs).
Unless we are talking about very specific tasks, any 6-year-old is infinitely more flexible and general in his or her intelligence than the "smartest" Amazon robot.
Throughout the 1960s, progress seemed to come swiftly as researchers developed computer systems that could play chess, deduce mathematical theorems, and even engage in simple discussions with a person. Government funding flowed generously. Optimism was so high that, in 1970, Minsky famously proclaimed, "In three to eight years we will have a machine with the general intelligence of a human being."
By the mid 1970s, however, it was clear that Minsky's optimism was unwarranted. Progress stalled as many of the innovations of the previous decade proved too narrow in their applicability, seeming more like toys than steps toward a general version of artificial intelligence. Funding dried up so completely that researchers soon took pains not to refer to their work as AI, as the term carried a stink that killed proposals.
The cycle repeated itself in the 1980s with the rise of expert systems and the renewed interest in what we now call neural networks (i.e., programs based on connectivity architectures that mimic neurons in the brain). Once again, there was wild optimism and big increases in funding. What was novel in this cycle was the addition of significant private funding as more companies began to rely on computers as essential components of their business. But, once again, the big promises were never realized, and funding dried up again.
AI: Hype vs. reality
The AI summer we're currently experiencing began sometime in the first decade of the new millennium. Vast increases in both computing speed and storage ushered in the era of deep learning and big data. Deep learning methods use stacked layers of neural networks that pass information to each other to solve complex problems like facial recognition. Big data provides these systems with vast oceans of examples (like images of faces) to train on. The applications of this progress are all around us: Google Maps give you near-perfect directions; you can talk with Siri anytime you want; IBM's Deep Think computer beat Jeopardy's greatest human champions.
In response, the hype rose again. True AI, we were told, must be just around the corner. In 2015, for example, The Guardian reported that self-driving cars, the killer app of modern AI, was close at hand. Readers were told, "By 2020 you will become a permanent backseat driver." And just two years ago, Elon Musk claimed that by 2020 "we'd have over a million cars with full self-driving software."
The general intelligence — i.e., the understanding — we humans exhibit may be inseparable from our experiencing. If that's true, then our physical embodiment, enmeshed in a context-rich world, may be difficult if not impossible to capture in symbolic processing systems.
By now, it's obvious that a world of fully self-driving cars is still years away. Likewise, in spite of the remarkable progress we've made in machine learning, we're still far from creating systems that possess general intelligence. The emphasis is on the term general because that's what AI really has been promising all these years: a machine that's flexible in dealing with any situation as it comes up. Instead, what researchers have found is that, despite all their remarkable progress, the systems they've built remain brittle, which is a technical term meaning "they do very wrong things when given unexpected inputs." Try asking Siri to find "restaurants that aren't McDonald's." You won't like the results.
Unless we are talking about very specific tasks, any 6-year-old is infinitely more flexible and general in his or her intelligence than the "smartest" Amazon robot.
Even more important is the sense that, as remarkable as they are, none of the systems we've built understand anything about what they are doing. As philosopher Alva Noe said of Deep Think's famous Jeopardy! victory, "Watson answered no questions. It participated in no competition. It didn't do anything. All the doing was on our side. We played Jeapordy! with Watson." Considering this fact, some researchers claim that the general intelligence — i.e., the understanding — we humans exhibit may be inseparable from our experiencing. If that's true, then our physical embodiment, enmeshed in a context-rich world, may be difficult if not impossible to capture in symbolic processing systems.
Not the (AI) winter of our discontent
Thus, talk a of a new AI winter is popping up again. Given the importance of deep learning and big data in technology, it's hard to imagine funding for these domains drying up any time soon. What we may be seeing, however, is a kind of AI autumn when researchers wisely recalibrate their expectations and perhaps rethink their perspectives.
A new study explores how investors' behavior is affected by participating in online communities, like Reddit's WallStreetBets.
- The study found evidence that "hype" over assets is psychologically contagious among investors in online communities.
- This hype is self-perpetuating: A small group of investors hypes an asset, bringing in new investors, until growth becomes unsteady and a price crash ensues.
- The researchers suggested that these new kinds of self-organized, social media-driven investment behaviors are unlikely to disappear anytime soon.
Social media has reshaped human behavior in ways we're only starting to understand. The proliferation of online communities has helped spawn novel strategies for promoting political causes, conducting business, finding sex and love, and transforming culture.
Could online communities also transform behavior in the financial world?
That's one of the key questions explored in a new study published on the preprint server arXiv. Titled "Reddit's self-organised bull runs: Social contagion and asset prices," the study used discussion data from the subreddit WallStreetBets to analyze relationships between the price of stocks and "hype" among online retail investors.
Hype is nothing new in the investing world. But the researchers noted that there seems to be something novel about the short squeeze of GameStop's stock in January, when the price of the stock rose tenfold, thanks largely to self-organized retail investors from WallStreetBets.
"As academics and regulators alike grapple with the implications, many wonder whether large-scale coordination among retail investors is the new 'modus operandi,' or a one-off fluke," the researchers wrote. "We argue that this is a new manifestation of a well-established global phenomenon."
To better understand how online hype is associated with stock prices, the researchers focused on two social components of hype: contagion and consensus. Contagion refers to investors spreading interest in an asset among each other, while consensus refers to their ability to agree on whether to buy or sell an asset.
The analysis found empirical evidence that both contagion and consensus emerge in online communities like WallStreetBets. In other words, investors spread sentiments about future stock performance to other investors, and then they cohere around investment strategies.
Popularity over fundamentals
The findings suggest that an asset's popularity, not its fundamentals, is paramount to many investors.
"Our results consistently show that investors become interested in discussing an asset, not because of fundamentals, but because other users discuss it," the researchers wrote. "Subsequently, this paper tests whether an individual's sentiment about future asset performance [is] affected by those of others. We find that this is the case: people look to their peers to form an opinion about an asset's potential."
To find evidence for social contagion among online investors, the researchers compiled a large dataset of posts and comments submitted to WallStreetBets. The goal was to analyze whether investors' past comments or posts about a given stock, such as Tesla, had a predictable effect on future discussions of that asset within WallStreetBets.
After conducting a regression analysis, the results suggest that hype is socially contagious and cyclical. The cycle usually plays out like this: A small group of investors hypes an asset. This attracts a larger group of investors who join the discussions.
But eventually, too many investors have joined the discussion, and fewer new investors are buying into the hype. As investors lose interest, they spend less time discussing (or "spreading") the asset on the forum, and they turn to new opportunities. The process is similar to a virus: As enough people become infected, they reach herd immunity, and the virus (hype) dies out.
So, does this process affect the stock price, and if so, how? The researchers said it was difficult to establish causality between hype and actual market activity. After all, they didn't have access to the trading records of subscribers to WallStreetBets.
But their model did show that activity on WallStreetBets was able to explain "significant variance" in trading volumes for the most-discussed assets on the forum. This suggests that when social contagion is strong for a given asset, consensus is strong too.
On the stock chart, consensus may start off bullish (or positively): As hype spreads, there's a slow, steady run-up in price. But the growth eventually becomes unstable and is followed by a crash and a period of volatility.
"The price crash stems from panic selling, as investors turn nervous in the face of volatility," the researchers wrote.
Bad news spreads faster than good news
Interestingly, the analysis found that bearish (or negative) sentiments were significantly more contagious on WallStreetBets.
"The data demonstrates that authors who previously commented on a bearish post are 47.7% more likely to express bearish over neutral sentiments, and 18.1% less likely to express bullish sentiments over neutral sentiments. Similarly, but less markedly, authors who previously commented on at least one bullish submission are 9.4% more likely to write a bullish submission, yet 11.3% less likely to write a bearish one."
The researchers said that the changing investing climate and widely available online data offers "promising opportunities for future research."
"As social media galvanizes a larger pool of retail investors with the potential for exciting stock market gambles, it is crucial to understand how social dynamics can impact asset prices," the researchers wrote. "With the first publicly acclaimed victory of Main Street over Wall Street, in the form of the GameStop short squeeze, it is unlikely that socially-driven asset volatility will simply disappear."
A 19th-century surveying mistake kept lumberjacks away from what is now Minnesota's largest patch of old-growth trees.