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Decarbonization Stories
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Insight Stage 4 Dress Rehearsal Economy and Fossil Fuels
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Correlation of National Electrification with Internal and External Influence Factors
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Business Economic Sustainability
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This page provides a structural analysis of POTUS Candidate Hillary Clinton's economic policy based on the information at: https://www.hillaryclinton.com/the-four-fights/economy-of-tomorrow/
The method used is Integrative Propositional Analysis (IPA) available: ​ http://scipolicy.org/uploads/3/4/6/9/3469675/wallis_white_paper_-_the_ipa_answer_2014.12.11.pdf
DRAFT IPA of Hillary Clinton Economic Policy
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A simple model for cc adoption which depends on several condions.
Cloud Computing adoptation
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• This model examines how sustainable consumerism is from social, economic, and environmental aspects. The question in focus is "How will our second-hand clothing donations affect communities in developing countries, specifically Kenya?"

5 Stock Variables: 
• U.S. Consumers
• Multinational Corporations
• Overseas Factories
• Kenya

Highlight Findings: 
To sum up, there are 4 major problems associated to donations:
• 1. Source of problem is the consumer: Cheap deals attract hundreds of millions in revenue for fast fashion, and contribute to 100,000 tonnes of clothing to Kenya annually. 
• 2. Rapid consumerism leads to over-utilization of slowly-renewable resources, such as water.
• 3. Nearly 96% of textiles jobs are eradicated by the massive inflow of clothing donations to Kenya. 
• 4. The offshoring of textiles jobs enrages U.S. blue-collar workers, leading to the rise of protectionism.  



Environmental, social, and economic sustainability aspects of textiles donations
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This page provides a structural analysis of POTUS Candidate Jim Gilmore's economic policy based on the information at: http://www.gilmoreforamerica.com/jims-growth-code/  The method used is Integrative Propositional Analysis (IPA) available: ​ http://scipolicy.org/uploads/3/4/6/9/3469675/wallis_white_paper_-_the_ipa_answer_2014.12.11.pdf
DRAFT IPA of Jim Gilmore Economic Policy
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Based on the SIR (Susceptible, Infected, Recovered) model of disease, this is an upgraded model with more specifc vaeriables.
Insights:
When the growth rate and the number of the recovered is much larger than deaths, the economic activity remain steady growing.
Model of COVID-19 outbreak in Burnie Tasmania
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Systems Science CLD exercise 2024
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This model analyzes the interaction between climate change mitigation and adaptation in the land use sector using the concept of forest transition as a framework.
Forest Transition
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WIP replication of Khalid Saeed's draft paper presented by the Economics chapter of the SD Society in Sept 2019 youtube video
Unlinking public finance and taxation in fiat currency
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Simple causal loop diagram of a compound interest savings account.
Causal loop diagram of savings account
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SFR23 Scenario 1
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Multiscale view of Combined PH and Economic Views IM 70763  in preparation for integrating with Prevention Investment Framework (private) IM
Multiscale Zoomable Prevention Model View
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An initial study of the economics of single use coffee pods.
Nina Coffee Company Model *
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Storytelling of My Investigating Insight Theme
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When people talk about a government deficit, they forget that this is only one side of the ledger. On the other is a corresponding non-government SURPLUS. The money the government spends is not lost but shows up in the private sector as income. When one talks only of the deficit then one can understand that many think it should be reduced or even converted into a surplus, but reducing the government deficit reduces private sector income and a government surplus forces a deficit on the private sector with a potentially devastating effect on private sector wealth and economic activity.  Unless the economy is overheating, government deficits are usually healthy. For countries that run traditionally a trade deficit, such as the US they are necessary to maintain economic activity. Consider this fact: for almost all of past 40 years the US and the UK have run deficits without any harmful effects!

This video by professor Stephanie Kelton contains evidence that supports the modle.

https://www.youtube.com/watch?v=g6rlprwQB5E

The Dynamic that shows that Government Deficits benefit the Private Sector
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Model description: 

This model is designed to simulate the Covid-19 outbreak in Burnie, Tasmania by estimating several factors such as exposed population, infection rate, testing rate, recovery rate, death rate and immunity loss. The model also simulates the measures implemented by the government which will impact on the local infection and economy. 

 

Assumption:

Government policies will reduce the mobility of the population as well as the infection. In addition, economic activities in the tourism and hospitality industry will suffer negative influences from the government measures. However, essential businesses like supermarkets will benefit from the health policies on the contrary.

 

Variables:

Infection rate, recovery rate, death rate, testing rate are the variables to the cases of Covid-19. On the other hand, the number of cases is also a variable to the government policies, which directly influences the number of exposed. 

 

The GDP is dependent on the variables of economic activities. Nonetheless, the government’s lockdown measure has also become the variable to the economic activities. 

 

Interesting insights:

Government policies are effective to curb infection by reducing the number of exposed when the case number is greater than 10. The economy becomes stagnant when the case spikes up but it climbs up again when the number of cases is under control. 

Sample Model of COVID-19 outbreak in Burnie Tasmania by Yim Fong Ng (544885)
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This is a protoype of the bahai high level economic model in accordance to the bahai-economic principles.
Bahai high level economic model
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​BACKGROUND:

The following simulation model demonstrates the relationship between supply, demand and pricing within the real estate and housing world. I have based the model on a small city with a population of 100,000 residents as of 2015. 

AXIS:

X-Axis
The X-Axis shows the time. It begins in 2015 in the month of October and continues for 36 consecutive years. 

Y-Axis
There are 2 Y-Axis on this model. The left hand side relates to the price, demand, and supply, while the right hand side solely lists the population.

As you could see, this town has a population of 100,000 residents to-date. The bottom of the model shows a population loop that produces an exponential growth rate of 2.5%. This dynamic and growing city populates approximately 240,000 residents after 36 years.

MODEL

The model consists of 2 folders named: Buyers/Consumers & Suppliers/Producers. This first folder represents the 'Demand'. It includes a buyers growth rate, buyers interest increase and decrease, a price demand and the demand price. The formulas form an exponential rise in demand due to the rapid and continuous increase in population in this new city. As population increases, so does the demand from buyers. 

The second folder conveys the supply of houses. It includes a sophisticated loop of real estate. Residents who own houses in the market decide to sell the home. This becomes the Houses for sale, also known as the 'supply'. Those houses are sold and the sold houses re-enter the market and the loop continues. 

The supply has an inverse relationship with the price. When prices drop, supplies drop because the demand goes up. And when the price goes up, so does the supply. This will represent the growth of new houses in the market. 

PRICE

Note: The price is based on monthly rent rates.

The price is dependant on many variables. Most importantly, the supply and demand. It also includes factors such as expectations & the economic value of the house. I have included a stable, 'good' economic value for all homes as this fictional town is in a stable and growing area.

Price fluctuates throughout the entire simulation, however it also goes up in price. Over the years houses continue to rise in price while they regularly fluctuate. For example, in 2018 (3 years later), the max price for a home was: $4254.7 and min price was: $852.98. On the other hand, in October 2051 (36 years later), the max price was: $14906 and the min price was: $7661. (This is based on the following data: Houses for Sale: 500, Houses that have sold: 100, Houses in the Market: 730).

SLIDERS

There are 3 sliders on the bottom that could be altered. The simulation would react accordingly. The 3 sliders include changeable data on:
- Houses for Sale.
- Houses that have Sold.
- Houses in the Market.


Real Estate Simulation Assignment - Mitchell Bassil 43290264
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Project Stage 1
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Brainstorming - Scoping constraints of tradeoff microanalysis
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VA - socio-economic