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collapse of complex systems

Cause Description Solution
Nested conditionals A sequence of conditionals nested inside each other increases cognitive complexity. Refactor nested conditionals into a series of functions or extract complex conditionals into their own functions.
Unnecessary abstraction Overly complex abstractions can lead to confusion and cognitive overload. Keep abstractions simple and only use them when necessary.
Large functions Functions with too much code and too many responsibilities make it difficult to understand the code's purpose. Break large functions into smaller functions with a single responsibility.
Magic values Hard-coded values that aren't easily understood can make the code harder to understand. Assign magic values to named constants or enums.
Global state Global state makes it difficult to understand how data is being used throughout the codebase. Avoid using global state and prefer passing data through function arguments.
Inconsistent naming Inconsistent naming conventions make it difficult to understand the code's meaning. Use consistent naming conventions and follow language-specific guidelines.
Poorly documented code Lack of documentation makes it difficult to understand code. Write clear comments and documentation that describes the code's purpose and behavior.

santa fe


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We must surrender our hopes and expectations, as well as our fears, and march directly into disappointment, work with disappointment, go into it and make it our way of life. . . . If we can open, then we suddenly begin to see that our expectations are irrelevant compared with the reality of the situations we are facing. This automatically brings disappointment. Disappointment is the best chariot to use on the path of the dharma. It does not confirm the existence of our ego and its dreams.

Chögyam Trungpa Rinpoche”


    title Timeline of Events Leading to Eco Collapse
    dateFormat  YYYY-MM-DD
    axisFormat  %m/%d/%Y
    section Causes of Collapse
    Unsustainable resource use          :done,    des1, 2010-01-01, 2020-01-01
    Habitat destruction                 :done,    des2, 2010-01-01, 2020-01-01
    Pollution                           :done,    des3, 2010-01-01, 2020-01-01
    Climate change                      :done,    des4, 2010-01-01, 2020-01-01
    section Effects of Collapse
    Extinction of species               :done,    act1, 2020-01-01, 2040-01-01
    Ecosystem collapse                  :done,    act2, 2040-01-01, 2060-01-01
    Resource scarcity                   :done,    act3, 2060-01-01, 2080-01-01
    Mass migration                      :done,    act4, 2080-01-01, 2100-01-01


Pandemic Year Death Toll
Plague of Justinian 541-542 25-50 million
Black Death 1346-1353 75-200 million
Smallpox 1520 56 million
Spanish Flu 1918-1920 50 million
HIV/AIDS 1981-present 32 million (as of 2020)
COVID-19 2019-present 5.5 million (as of Feb 2022)

nautral disasters

Rank Event Date Location Death Toll
1 1931 China floods 1931 China 1,000,000 - 4,000,000
2 1970 Bhola Cyclone 1970 East Pakistan (now Bangladesh) 500,000 - 1,000,000
3 1556 Shaanxi earthquake 1556 China 830,000
4 2010 Haiti earthquake 2010 Haiti 230,000
5 2004 Indian Ocean earthquake and tsunami 2004 Indian Ocean 227,898
6 1920 Haiyuan earthquake 1920 China 200,000 - 240,000
7 526 Antioch earthquake 526 Byzantine Empire (now Turkey) 250,000
8 1976 Tangshan earthquake 1976 China 242,000
9 2013 Typhoon Haiyan 2013 Philippines 6,300 - 6,700
10 1991 Bangladesh cyclone 1991 Bangladesh 138,000
Rank Event Date Location Fatalities
1 2004 Indian Ocean earthquake and tsunami December 26, 2004 Indian Ocean 230,000
2 2011 Tōhoku earthquake and tsunami March 11, 2011 Japan 15,897
3 1883 eruption of Krakatoa and tsunami August 26, 1883 Sunda Strait, Indonesia 36,417
4 1993 Hokkaido Nansei-oki earthquake and tsunami July 12, 1993 Japan 230
5 2018 Sulawesi earthquake and tsunami September 28, 2018 Indonesia 4,340
6 1896 Sanriku earthquake and tsunami June 15, 1896 Japan 22,070
7 1946 Nankaidō earthquake and tsunami December 20, 1946 Japan 1,330
8 1868 Arica earthquake and tsunami August 13, 1868 Peru and Chile 25,000
9 2010 Chile earthquake and tsunami February 27, 2010 Chile 525
10 2018 Sulawesi earthquake and tsunami September 28, 2018 Indonesia 4,340
Rank Storm Name Date Location Death Toll
1 Bhola Cyclone Nov 1970 Bangladesh 500,000+
2 Haiphong Typhoon Oct 1881 Vietnam 300,000+
3 Typhoon Nina Aug 1975 China 229,000
4 Haiyan Typhoon Nov 2013 Philippines 6,300
5 Bangladesh Cyclone Apr 1991 Bangladesh 138,000
6 Nagasaki Typhoon Sep 1945 Japan 50,000+
7 Mitch Hurricane Oct 1998 Central America 11,000+
8 Nargis Cyclone May 2008 Myanmar 138,000+
9 Katrina Hurricane Aug 2005 United States 1,833
10 Audrey Hurricane Jun 1957 United States 416
Rank Fire Name Year Location Size (acres) Fatalities
1 Peshtigo Fire 1871 Wisconsin, United States 1,200,000 1,500+
2 Great Fire of 1910 1910 Idaho, Montana, United States 3,000,000 87
3 Black Saturday Bushfires 2009 Victoria, Australia 1,100,000 173
4 Hinckley Fire 1894 Minnesota, United States 250,000 418+
5 Waldo Canyon Fire 2012 Colorado, United States 18,247 2
6 Camp Fire 2018 California, United States 153,336 85
7 Cloquet Fire 1918 Minnesota, United States 100,000 453
8 Yacolt Burn 1902 Washington, United States 238,920 65+
9 Black Dragon Fire 1987 Heilongjiang, China 3,000,000 200+
10 Matilija Fire 1932 California, United States 220,000 0
Event Date Citation
Deforestation of the Amazon Rainforest ongoing
Deforestation of the Indonesian Rainforest ongoing
Deforestation of the Congo Rainforest ongoing
The Great Plains Dust Bowl 1930s
Deforestation of the Eastern United States 1600s - 1900s
Deforestation of the Brazilian Cerrado ongoing
Deforestation of Madagascar ongoing
Deforestation of Borneo ongoing
Deforestation of the Sahel ongoing
Deforestation of Haiti ongoing
Rank Event Year(s) Area Deforested
1 Deforestation of the Amazon rainforest 1970s-present 17%
2 Deforestation of the Indonesian rainforest 1950s-present 12%
3 Deforestation of the Congo rainforest 1900s-present 8%
4 Deforestation of the Atlantic Forest 1500s-present 93%
5 Deforestation of the Chaco 1980s-present 25%
6 Deforestation of the Gran Chaco 1900s-present 20%
7 Deforestation of the Mata Atlântica 1500s-present 88%
8 Deforestation of the Sundarbans 1900s-present 26%
9 Deforestation of the Caatinga 1950s-present 46%
10 Deforestation of the Cerrado 1960s-present 50%
Rank Event Date Location Death toll
1 Fukushima Daiichi nuclear disaster March 11, 2011 Japan 15,899
2 Chernobyl disaster April 26, 1986 Ukraine 4,000
3 Bhopal gas tragedy December 3, 1984 India 3,787
4 Rana Plaza building collapse April 24, 2013 Bangladesh 1,135
5 Sampoong Department Store collapse June 29, 1995 South Korea 502
6 Tenerife airport disaster March 27, 1977 Canary Islands, Spain 583
7 Hyatt Regency walkway collapse July 17, 1981 Kansas City, Missouri, USA 114
8 Pemberton Mill collapse January 10, 1860 Lawrence, Massachusetts, USA 145
9 Ytong factory collapse November 24, 1995 Lüneburg, Germany 5
10 Willow Island disaster April 27, 1978 Willow Island, West Virginia, USA 51

the visible money trail

Rank Achievement (world economic forum)
1 Partnering Against Corruption Initiative (PACI)
2 Global Battery Alliance
3 Tropical Forest Alliance 2020
4 Shaping the Future of Construction
5 Future of Electricity
6 Future of Health and Healthcare
7 New Vision for Education
8 Future of Food
9 Platform for Accelerating the Circular Economy (PACE)
10 Global Future Councils
Industrial Revolution Time Period Main Characteristics
First Industrial Revolution 1760-1840 Transition from manual production to machines, steam power, and factories
Second Industrial Revolution Late 19th - Early 20th Century Advancements in steel production, electricity, and mass production
Third Industrial Revolution 1969-present Rise of electronics, telecommunications, and automation
Fourth Industrial Revolution Ongoing since late 20th century Emergence of digital technologies, artificial intelligence, and interconnectedness of devices
Rank Country GDP (nominal)
1 United States $22.675 trillion
2 China $16.645 trillion
3 Japan $4.872 trillion
4 Germany $4.170 trillion
5 United Kingdom $2.622 trillion
6 India $2.611 trillion
7 France $2.582 trillion
8 Italy $1.935 trillion
9 Canada $1.655 trillion
10 South Korea $1.586 trillion
Rank Organization Lobbying Expenditures
1 U.S. Chamber of Commerce $39,320,000
2 National Association of Realtors $36,190,000
3 Pharmaceutical Research & Manufacturers of America $25,891,272
4 American Medical Association $22,300,000
5 American Hospital Association $19,815,000
6 Business Roundtable $19,530,000
7 Blue Cross Blue Shield $18,910,000
8 Koch Industries $17,910,000
9 National Association of Broadcasters $17,620,000
10 American Chemistry Council $16,880,000
Rank Organization Lobbying Expenditures
1 U.S. Chamber of Commerce $82,920,000
2 National Association of Realtors $74,435,514
3 Pharmaceutical Research & Manufacturers of America $27,970,000
4 American Medical Association $24,374,000
5 Blue Cross Blue Shield $23,040,000
6 American Hospital Association $21,518,719
7 American Association for Justice $18,470,000
8 National Association of Home Builders $16,620,000
9 American Chemistry Council $15,610,000
10 National Association of Broadcasters $15,545,000

manmade disasters

Rank Event Date Location Death Toll
1 Chernobyl disaster April 26, 1986 Pripyat, Ukraine 4,000 (estimated)
2 Bhopal gas tragedy December 3, 1984 Bhopal, India 15,000 (estimated)
3 Tangshan earthquake July 28, 1976 Tangshan, China 242,769
4 Cyclone Nargis May 2-3, 2008 Myanmar 138,366
5 The Great Leap Forward 1958-1962 China 30 million (estimated)
6 The Holocaust 1941-1945 Europe 11 million (estimated)
7 Soviet famine of 1932-33 1932-1933 Soviet Union 7 million (estimated)
8 Hiroshima bombing August 6, 1945 Hiroshima, Japan 140,000 (estimated)
9 Nagasaki bombing August 9, 1945 Nagasaki, Japan 70,000 (estimated)
10 Three Gorges Dam construction 1994-ongoing Yangtze River, China 1.3 million (estimated)

another version

Rank Event Date Location Death Toll
1 The Great Chinese Famine 1959-1961 China 15-45 million
2 World War II 1939-1945 Worldwide 70-85 million
3 Spanish Flu Pandemic 1918-1920 Worldwide 17-50 million
4 Mao Zedong's regime 1949-1976 China 40-70 million
5 Soviet famine of 1932-33 1932-1933 Soviet Union 7 million
6 Hiroshima and Nagasaki bombings August 6 and 9, 1945 Japan 129,000-226,000
7 Partition of India August 1947 India and Pakistan 1 million
8 Armenian genocide 1915-1917 Ottoman Empire 1.5 million
9 Rwandan genocide April-July 1994 Rwanda 800,000
10 Cambodian genocide 1975-1979 Cambodia 1.7 million


Rank Event Date Location Death toll
1 Wall Street Crash of 1929 October 24, 1929 New York City, New York, United States N/A
2 Ponzi Scheme by Bernie Madoff December 11, 2008 New York City, New York, United States N/A
3 Savings and Loan Crisis of the 1980s and 1990s 1980s-1990s United States N/A
4 Enron Scandal October 16, 2001 Houston, Texas, United States N/A
5 Global Financial Crisis of 2008 2008-2009 Worldwide N/A
6 Lehman Brothers Collapse September 15, 2008 New York City, New York, United States N/A
7 WorldCom Accounting Scandal 2002 Clinton, Mississippi, United States N/A
8 Long-Term Capital Management Hedge Fund Collapse 1998 Greenwich, Connecticut, United States N/A
9 Mad Cow Disease Outbreak 1986-2006 Worldwide 227 deaths
10 BP Oil Spill April 20, 2010 Gulf of Mexico, United States 11 deaths


Rank Event Date Location Death Toll
1 Great Chinese Famine 1958-1962 China 15-45 million
2 Irish Potato Famine 1845-1852 Ireland 1 million
3 Bengal Famine of 1943 1943 India and Bangladesh 2-3 million
4 Holodomor (Ukrainian Famine) 1932-1933 Ukraine 3.3-7.5 million
5 Ethiopian Famine of 1984-1985 1984-1985 Ethiopia 400,000-1.2 million
6 North Korean Famine 1994-1998 North Korea 240,000-3.5 million
7 Sahel Famine 1968-1974 West Africa 100,000-1 million
8 Great Famine of 1876-1878 1876-1878 India 5.5 million
9 Soviet Famine of 1932-1933 1932-1933 Soviet Union 6-8 million
10 Madhya Pradesh famine 1943-1944 India 1.5-2 million


Rank Event Date Location Death toll
1 Bhopal disaster Dec 3, 1984 Bhopal, India 3,787
2 Chernobyl disaster Apr 26, 1986 Pripyat, Ukraine 4,000
3 Fukushima Daiichi nuclear disaster Mar 11, 2011 Fukushima, Japan 1,600
4 Deepwater Horizon oil spill Apr 20, 2010 Gulf of Mexico 11
5 Sidoarjo mud flow disaster May 29, 2006 Sidoarjo, Indonesia 15
6 Texas City refinery explosion Apr 16, 1947 Texas City, Texas 581
7 Exxon Valdez oil spill Mar 24, 1989 Prince William Sound, Alaska 11
8 Piper Alpha oil rig disaster Jul 6, 1988 North Sea 167
9 Baia Mare cyanide spill Jan 30, 2000 Baia Mare, Romania 2
10 Buncefield oil depot explosion Dec 11, 2005 Hertfordshire, UK 0

top earthquakes

Rank Magnitude Date Location Death Toll
1 9.5 May 22, 1960 Valdivia, Chile 5,700+
2 9.2 March 27, 1964 Prince William Sound, Alaska 131
3 9.1 December 26, 2004 Off the west coast of northern Sumatra 230,000+
4 9.0 March 11, 2011 Honshu, Japan 15,896
5 8.8 February 27, 2010 Maule Region, Chile 525
6 8.6 August 15, 1950 Assam, India-Tibet 780
7 8.5 November 25, 1833 Sumatra, Indonesia 5,000+
8 8.5 January 31, 1906 Ecuador-Colombia border region 1,000+
9 8.5 February 4, 1965 Rat Islands, Alaska 0
10 8.5 March 28, 2005 Northern Sumatra, Indonesia 1,313


Greed is often caused by a desire for more wealth or possessions, as well as a fear of not having enough. It can also be influenced by societal and cultural values, such as a focus on material success and competition. Additionally, some research suggests that biological factors, such as genetics and brain chemistry, may play a role in shaping individuals' propensity for greed.

Rank Name Net Worth (Billion USD) Source of Wealth
1 Elon Musk 195.7 Tesla, SpaceX, Neuralink
2 Jeff Bezos 191.6 Amazon
3 Bernard Arnault & Family 178.2 LVMH
4 Bill Gates 130.9 Microsoft
5 Mark Zuckerberg 106.4 Facebook
6 Warren Buffett 94.7 Berkshire Hathaway
7 Larry Ellison 84.6 Oracle
8 Larry Page 79.4 Google
9 Sergey Brin 76.8 Google
10 Steve Ballmer 71.6 Microsoft


Event Date Citation
Standard Oil Company is incorporated January 10, 1870 Source
United States Steel Corporation is founded February 25, 1901 Source
American Tobacco Company is formed January 31, 1902 Source
International Mercantile Marine Company is formed April 1902 Source
Northern Securities Company is formed November 1901 Source
Radio Corporation of America (RCA) is formed October 17, 1919 Source
Aluminum Company of America (Alcoa) is formed October 1, 1888 Source
International Business Machines Corporation (IBM) is founded June 16, 1911 Source
Bell Telephone Company is founded July 9, 1877 Source
Microsoft Corporation is founded April 4, 1975 Source

coca cola

Company Business
Costa Coffee Coffee Shops
Honest Tea Bottled Tea
Minute Maid Fruit Beverages
Simply Orange Orange Juice
SmartWater Bottled Water
Vitamin Water Flavored Water
Zico Coconut Water
Odwalla Smoothies and Juices
FUZE Tea Bottled Tea
Innocent Drinks Smoothies and Juices


Company Name Type of Business
Pepsi-Cola Beverage Manufacturing
Frito-Lay Snack Food Manufacturing
Tropicana Juice Manufacturing
Gatorade Sports Drink Manufacturing
Quaker Foods Cereal and Snack Manufacturing
Sabra Dipping Company Hummus and Dips Manufacturing
Naked Juice Juice and Smoothie Manufacturing
KeVita Probiotic Drinks Manufacturing
Lipton Tea Manufacturing
Rockstar Energy Energy Drink Manufacturing
Starbucks Ready-to-Drink Beverages Bottled Coffee and Tea Manufacturing
Muscle Milk Protein Drink Manufacturing
Bang Energy Energy Drink Manufacturing
Izze Sparkling Juice Manufacturing
Hilo Life Keto Snack Manufacturing
Brand Product Type
Pepsi Carbonated Soft Drink
Lay's Potato Chips
Tostitos Tortilla Chips
Doritos Tortilla Chips
Cheetos Cheese Snacks
Quaker Oats and Snacks
Gatorade Sports Drink
Tropicana Juice
Naked Juice Juice
Mountain Dew Carbonated Soft Drink
7UP Carbonated Soft Drink
Mirinda Carbonated Soft Drink
Aquafina Bottled Water
Lipton Iced Tea
Brisk Iced Tea
Starbucks Bottled Coffee
Rockstar Energy Drink

yum brand

Restaurant Type
KFC Fast Food
Taco Bell Fast Food
Pizza Hut Fast Casual
The Habit Burger Fast Casual


Event Date Description Consequences Citation
South Sea Bubble 1720 A stock market bubble in England Resulted in the bankruptcy of many investors, including the government and the Company of the Bank of England source
Mississippi Company 1719-1720 A speculative bubble in France Led to the bankruptcy of the company and the collapse of the French economy source
Panic of 1907 1907 A financial crisis in the United States Led to the creation of the Federal Reserve System to prevent future financial crises source
Tulip Mania 1637 A speculative bubble in the Netherlands Led to the collapse of the Dutch tulip market source
Wall Street Crash of 1929 1929 A stock market crash in the United States Led to the Great Depression and a decade of economic hardship source

manipulating prices

Vincent Kosuga was a farmer who became known for his attempt to manipulate the potato market in the United States in the 1950s. He managed to gain control of a large portion of the potato market by buying up the entire potato crop from farmers across the country and holding onto it in storage facilities. He then waited for the potato prices to rise before selling the potatoes for a profit.

Kosuga's actions were not illegal, but they did raise concerns about the potential for individuals to manipulate commodity markets in this way. As a result of his actions, the government introduced new regulations to prevent market manipulation in the future.

Silos are used to manipulate agriculture prices by allowing buyers to hold onto their stocks of crops until prices increase. This is known as hoarding, and it can lead to a shortage of crops in the market, which drives prices up. By controlling the supply of crops, silos can create an artificial shortage, allowing the owners to charge higher prices. Additionally, silos can be used to store crops until they can be sold at a more favorable price, further manipulating the market.

diamond silo

There is no way to accurately determine the value of diamonds that are kept in vaults and not sold, as this information is not publicly available. Diamond companies and traders generally do not disclose the exact amount of inventory they hold, and the value of diamonds can also vary depending on a number of factors such as size, color, and clarity. Additionally, the value of diamonds can fluctuate over time due to changes in market demand and other economic factors.


what creature could most likely end humanity? ...most likely it's ourselves

According to the World Health Organization (WHO), mosquitoes are responsible for an estimated 725,000 deaths per year.

However, if we consider the indirect impact of a species on human mortality (i.e., the impact on human health caused by a species, but not directly causing death), then humans themselves would be the species that kills the most humans, through wars, homicides, and other forms of violence.

If we consider only direct mortality (i.e., death caused by a specific species), the answer is mosquitoes, which are responsible for spreading diseases such as malaria, dengue, yellow fever, and Zika virus, among others.

It's important to note that the numbers for both of these scenarios are estimates and can vary widely depending on the sources and methods used to gather data.

Additionally, there are other species, such as snakes, crocodiles, and sharks, which can also pose a significant threat to human life in certain regions or circumstances.

top 10 natural disasters

this makes me want to move: I live on a faultline, near a volcano, near the ocean, in my free time I often section hike through the mountains, several years my hikes were cut short due to forest fires

  • Earthquakes: Earthquakes can cause widespread destruction, loss of life, and trigger secondary events such as tsunamis and landslides.
  • Tsunamis: Tsunamis are large ocean waves caused by earthquakes, volcanic eruptions, and meteor impacts, which can cause widespread destruction and loss of life along coastlines.
  • Hurricanes: Hurricanes are intense tropical storms that can cause widespread damage and flooding through strong winds and heavy rainfall.
  • Cyclones: Cyclones are similar to hurricanes but form in the Indian Ocean and the South Pacific.
  • Tornadoes: Tornadoes are rapidly rotating columns of air that can cause extensive damage through high winds.
  • Wildfires: Wildfires can cause widespread destruction of property and wildlife habitats, as well as release harmful pollutants into the atmosphere.
  • Floods: Floods occur when bodies of water overflow their banks and inundate nearby areas, causing widespread damage and displacement of populations.
  • Volcanic eruptions: Volcanic eruptions can cause widespread damage and loss of life through ash fall, lava flows, and the creation of tsunamis.
  • Landslides: Landslides are the movement of rock, soil, and other materials down a slope, which can cause damage to structures and infrastructure.
  • Droughts: Droughts are prolonged periods of dry weather that can lead to crop failure, famine, and water shortages.
Effect of Ocean Rising 10 Inches
Coastal flooding and erosion
Saltwater intrusion into crops
Increased storm surge damage
Loss of habitat for marine life
Increased ocean acidification
Displacement of coastal communities
Water quality issues
Impacts on fishing industry
Damage to coastal infrastructure
Increase in climate refugees

There are several factors that contribute to the ecological collapse of a region or the planet as a whole. Here are some of the most significant ones:

  • Climate change: The burning of fossil fuels and deforestation are leading to rising levels of greenhouse gases, which are causing the planet to warm. This is leading to changes in weather patterns, melting of polar ice caps, and rising sea levels, which can have devastating impacts on ecosystems and the species that inhabit them.
  • Habitat destruction: The destruction of natural habitats, such as forests, wetlands, and prairies, is causing the loss of biodiversity and making it difficult for species to adapt to changing conditions.
  • Overfishing: Overfishing can cause a decline in fish populations, which can have ripple effects throughout ocean ecosystems and disrupt food webs.
  • Pesticides and toxic chemicals: The widespread use of pesticides and other toxic chemicals can have negative impacts on wildlife, as well as on human health.
  • Invasive species: Non-native species introduced into new environments can outcompete native species and alter ecosystems, often with negative impacts.
  • Pollution: Pollution from sources such as factories, sewage treatment plants, and oil spills can have detrimental effects on wildlife and ecosystems.
  • Overexploitation of resources: The extraction of resources such as water, timber, and minerals, can have negative impacts on ecosystems, leading to soil degradation and loss of biodiversity.
  • Soil degradation: Soil degradation from factors such as deforestation, overgrazing, and improper agriculture practices can result in the loss of fertile land and make it difficult for plants and animals to thrive.
  • Ocean acidification: The increase in carbon dioxide levels in the atmosphere is causing the oceans to become more acidic, which can have negative impacts on marine life and disrupt ocean ecosystems.
  • Human population growth: The growing human population is putting increasing pressure on the planet's resources, leading to habitat destruction, deforestation, and overexploitation of resources, which can have negative impacts on the environment and wildlife.

Addressing these factors is critical for avoiding ecological collapse and ensuring the health and sustainability of ecosystems and the species that inhabit them. This will require a concerted effort from individuals, governments, and businesses to reduce emissions, conserve resources, and protect habitats.

Rank Country CO2 Emissions (in metric tons)
1 China 10,723,821,100
2 United States 5,416,968,300
3 India 2,654,469,500
4 Russia 1,711,083,600
5 Japan 1,191,114,200
6 Germany 801,172,200
7 Iran 720,427,200
8 South Korea 649,155,200
9 Saudi Arabia 592,480,100
10 Canada 563,498,200


Rank Date Location Event Toxic Material Fatalities Notes
1 1984 Bhopal, India Gas Leak Disaster Methyl Isocyanate 4,000-16,000 Considered the world's worst industrial disaster
2 1978 Love Canal, USA Chemical Waste Dumping Dioxin, Benzene, Toluene, Xylene, PCBs Unknown Resulted in the creation of the Superfund program
3 1996 Baia Mare, Romania Cyanide Spill Cyanide 0 Released over 100,000 cubic meters of cyanide-contaminated waste water into a river
4 2010 Gulf of Mexico, USA Deepwater Horizon Oil Spill Crude Oil 11 Considered the largest marine oil spill in history
5 2015 Minamata Bay, Japan Mercury Poisoning Methyl Mercury Unknown Thousands affected by eating contaminated fish
6 1956 Minamata, Japan Mercury Poisoning Methyl Mercury Unknown Discharged into Minamata Bay by a chemical factory
7 1986 Chernobyl, Ukraine Nuclear Disaster Radioactive Material 4,000 Considered the worst nuclear power plant accident in history
8 2006 Abidjan, Ivory Coast Toxic Waste Dumping Unknown Unknown Over 100,000 people affected by illegal dumping of toxic waste
9 1985 Pemex Refinery, Mexico Explosion and Fire Hydrocarbon 500-600 One of the worst industrial disasters in Mexico
10 1976 Seveso, Italy Dioxin Release Dioxin 37 One of the highest recorded levels of dioxin exposure in humans


Carbon-sucking unicorns

Carbon-sucking unicorns is a colloquial term that refers to unproven, highly speculative technologies that are promoted as a solution to the problem of climate change and carbon emissions. These technologies are often described as "miracle" or "silver bullet" solutions that will solve the problem of climate change without requiring significant changes to the current system.

Examples of carbon-sucking unicorns include the idea of large-scale deployment of carbon capture and storage (CCS) technologies, or the idea of "geoengineering" the climate by, for example, injecting reflective particles into the atmosphere to reflect sunlight back into space.

While these technologies may have some potential to address the problem of climate change, they are often criticized for being unrealistic, unproven, and based on a limited understanding of the complex and interconnected systems that regulate the Earth's climate. In addition, many of these technologies are seen as a way to avoid taking responsibility for reducing emissions and making the necessary changes to our lifestyles and economic systems.

It is important to remember that there is no single solution to the problem of climate change, and that a comprehensive and multi-faceted approach is necessary to address the problem effectively. This includes reducing emissions, promoting energy efficiency and renewable energy, and protecting and restoring ecosystems that help to regulate the Earth's climate.


Techno-idolatry refers to the worship or excessive veneration of technology and technological advancements, often to the point of disregarding other important values and considerations. It involves the belief that technology can solve all of society's problems, and that technology is an unmitigated good that can provide unlimited benefits.

Techno-idolatry is often accompanied by a belief in the unlimited potential of technology, and a disregard for the potential downsides and negative consequences of technological progress. For example, some proponents of techno-idolatry may view issues such as climate change, resource depletion, and social inequality as problems that can be solved through technological solutions alone, without considering the need for changes in economic systems, political structures, and individual behavior.

This approach to technology can be problematic because it ignores the fact that technology is not neutral, and that it can have unintended consequences that may harm the environment and society. It can also lead to a blind faith in technology, without critically examining its limitations and potential impacts.

To avoid the negative consequences of techno-idolatry, it is important to adopt a more balanced and nuanced approach to technology, one that recognizes its benefits while also acknowledging its limitations and potential downsides. This involves considering the ethical, social, and environmental implications of technological advancements, and striving to use technology in a responsible and sustainable way.

us protests

Rank Protest Event Date Location Estimated Attendance
1 Women's March January 21, 2017 Washington, D.C. 470,000 - 680,000
2 March for Our Lives March 24, 2018 Washington, D.C. 800,000
3 The People's Climate March April 29, 2017 Washington, D.C. 200,000
4 Tax March April 15, 2017 Washington, D.C. 100,000
5 Families Belong Together June 30, 2018 Various Cities 100,000 - 700,000
6 March for Science April 22, 2017 Various Cities 1 million
7 Black Lives Matter Summer 2020 Various Cities Millions
8 Occupy Wall Street September 2011 New York City Thousands
9 Anti-War Protests 2002-2003 Various Cities Millions
10 March on Washington for Jobs and Freedom August 28, 1963 Washington, D.C. 200,000

The Seneca Curve

Growth is slow, while the road to ruin is fast.

  • seneca, philosopher 4bc

The Seneca Curve is a graphical representation of the idea that growth and collapse are inherent in complex systems, including human societies. The Seneca Curve is named after the Roman philosopher Seneca, who is said to have observed that growth in wealth and power is followed by a rapid decline.

The Seneca Curve is typically depicted as a graph that starts with slow growth, followed by a period of rapid expansion, and then a sharp decline. This pattern is thought to be the result of various factors, including the depletion of resources, the accumulation of waste, the loss of resilience and adaptability, and the creation of unsustainable systems.

The Seneca Curve has been applied to the study of the collapse of civilizations, including the Roman Empire, as well as to contemporary global challenges such as climate change, resource depletion, and economic instability. By understanding the dynamics of growth and collapse in complex systems, it is possible to develop strategies for avoiding or mitigating the consequences of collapse, and promoting long-term sustainability.

cargo / globalization

The amount of gas a cargo ship would use on a trip depends on several factors such as the distance traveled, the size and efficiency of the ship's engines, the weather conditions, and the type of fuel used. However, as a rough estimate, a cargo ship carrying 200,000,000 tons of cargo could use anywhere from 1,000 to 3,000 metric tons of fuel per day, depending on the factors mentioned above. This means that for a trip of, say, 10 days, the ship could use anywhere from 10,000 to 30,000 metric tons of fuel.

The amount of CO2 produced by burning 30,000 metric tons of fuel would depend on the type of fuel being burned.

Assuming the fuel being burned is diesel, which produces approximately 2.68 kg of CO2 per liter of fuel burned, the amount of CO2 produced by burning 30,000 metric tons (30 million kilograms) of diesel would be approximately:

30,000,000 kg x 2.68 kg CO2/liter = 80,400,000 kg or 80,400 metric tons of CO2.

Note: TEU stands for "Twenty-foot Equivalent Unit" and is a measure of a ship's cargo carrying capacity.

Rank Ship Name Fuel Capacity (metric tons) Cargo Capacity (TEU)
1 HMM Algeciras 17,500 23,964
2 MSC Gülsün 23,756 23,756
3 MSC Mina 20,000 23,756
4 OOCL Hong Kong 18,000 21,413
5 CMA CGM Antoine de Saint Exupery 18,000 20,776
6 Ever Golden 20,388 20,124
7 COSCO Shipping Universe 20,119 19,100
8 COSCO Shipping Virgo 20,000 19,000
9 OOCL Germany 18,000 21,413
10 MSC Anna 18,000 19,224

MSC Gülsün co2 emissions for example: burning 23,000 metric tons of crude oil would produce approximately 2,308 metric tons of CO2.

Rank Port Name Weight of Cargo (in tons) Type of Cargo
1 South Louisiana 251,243,271 Petroleum, chemicals, coal, grain, and other dry goods
2 Houston 225,603,757 Petroleum, chemicals, and coal
3 New York/New Jersey 161,453,662 Petroleum, chemicals, and vehicles
4 Beaumont 91,294,291 Petroleum and chemicals
5 Corpus Christi 84,229,610 Petroleum, chemicals, and grain
6 New Orleans 77,267,910 Petroleum, chemicals, and grain
7 Long Beach/Los Angeles 69,307,234 Petroleum, chemicals, vehicles, and other dry goods
8 Baton Rouge 58,466,552 Petroleum, chemicals, and grain
9 Port Arthur 51,585,801 Petroleum, chemicals, and coal
10 Mobile 44,789,271 Chemicals and petroleum
Rank Ship Name Cargo Capacity (TEU) Year Built Flag
1 MSC Gülsün 23,756 2019 Panama
2 OOCL Hong Kong 21,413 2017 Hong Kong
3 COSCO Shipping Universe 21,237 2018 Hong Kong
4 Madrid Maersk 20,568 2017 Denmark
5 Ever Golden 20,388 2020 Panama
6 MOL Triumph 20,170 2017 Panama
7 CMA CGM Antoine de Saint Exupéry 20,078 2018 France
8 CMA CGM Jacques Saadé 20,000 2020 France
9 MSC Isabella 19,630 2019 Panama
10 MSC Sixin 19,574 2019 Panama
Rank Flight Cargo Type Fuel Capacity (gallons) Plane Model
1 Emirates SkyCargo General Cargo 324,848 777-200LR
2 Qatar Airways Cargo Fresh Produce 265,000 747-8F
3 Etihad Cargo General Cargo 222,000 777-200F
4 Cargolux Italia General Cargo 220,000 747-400F
5 Korean Air Cargo General Cargo 200,000 747-8F
6 Cathay Pacific Cargo General Cargo 205,000 747-8F
7 Turkish Airlines Cargo General Cargo 200,000 A330-200F
8 Lufthansa Cargo General Cargo 179,000 777F
9 AirBridgeCargo Airlines General Cargo 168,000 747-8F
10 Atlas Air General Cargo 159,000 747-8F
Rank Name Primary Income Net Worth (USD)
1 Elon Musk Tesla, SpaceX 195B
2 Jeff Bezos 185B
3 Bernard Arnault LVMH 150B
4 Bill Gates Microsoft 124B
5 Mark Zuckerberg Facebook 96B
6 Warren Buffett Berkshire Hathaway 96B
7 Larry Ellison Software 93B
8 Larry Page Google 91.5B
9 Sergey Brin Google 89B
10 Mukesh Ambani Diversified 87B
11 Steve Ballmer Microsoft 86.8B
12 Jim Walton Walmart 81.7B
13 Alice Walton Walmart 81.5B
14 Rob Walton Walmart 81.3B
15 Francoise Bettencourt Meyers L'Oreal 71.7B
16 Amancio Ortega Zara 71B
17 Zhong Shanshan Beverages 68.6B
18 Larry Fink BlackRock 61.9B
19 Carlos Slim Helu Telecom 59.2B
20 Steve Schwarzman Private Equity 34.3B
Rank Company Name Country Industry Estimated Annual Revenue
1 IBM Watson United States Enterprise AI $4 billion+
2 Microsoft United States Enterprise AI $3 billion+
3 Google United States Enterprise AI $1.2 billion+
4 Amazon Web Services United States Enterprise AI $1 billion+
5 Salesforce United States Enterprise AI $500 million+
6 Apple United States Consumer AI $300 million+
7 Baidu China Consumer AI $270 million+
8 Tencent China Enterprise AI $240 million+
9 Intel United States Enterprise AI $200 million+
10 Facebook United States Consumer AI $180 million+
11 SAP Germany Enterprise AI $165 million+
12 Nvidia United States Hardware for AI $160 million+
13 Samsung South Korea Enterprise AI $140 million+
14 Alibaba Cloud China Enterprise AI $135 million+
15 SenseTime China Enterprise AI $120 million+
16 Huawei China Enterprise AI $115 million+
17 OpenAI United States Research $100 million+
18 IBM (other) United States Research $90 million+
19 Wipro India Enterprise AI $80 million+
20 Tencent Cloud China Enterprise AI $75 million+