Stephen P Dunn 2010 Book summary including Technostructure MMT PCT critical realist and managing perceptions links

Revised version of S&F diagram, with Renewable energy investment as an auxiliary variable.

Ocean/atmosphere/biosphere model tuned for interactive economics-based simulations from Y2k on.

Modelo de aposentadoria complementar para o curso de DS (Sistemas Dinâmicos).

Eastern oyster growth model calibrated for Long Island Sound
Developed and implemented by Joao G. Ferreira and Camille Saurel; growth data from Eva Galimany, Gary Wickfors, and Julie Rose; driver data from Julie Rose and Suzanne Bricker; Culture practice from the REServ team and Tessa Getchis. This model is a workbench for combining ecological and economic components for REServ. Economic component added by Trina Wellman.

This is a one box model for an idealized farm with one million oysters seeded (one hectare @ a stocking density of 100 oysters per square meter)

1. Run WinShell individual growth model for one year with Long Island Sound growth drivers;

2. Determine the scope for growth (in dry tissue weight per day) for oysters centered on the five weight classes)
 
3. Apply a classic population dynamics equation:

dn(s,t)/dt = -d[n(s,t)g(s,t)]/ds - u(s)n(s,t)

s: Weight (g)
t: Time
n: Number of individuals of weight s
g: Scope for growth (g day-1)
u: Mortality rate (day-1)

4. Set mortality at 30% per year, slider allows scenarios from 30% to 80% per year

5. Determine harvestable biomass, i.e. weight class 5, 40-50 g (roughly three inches length)

• 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?"

Ocean/atmosphere/biosphere model tuned for interactive economics-based simulations from Y2k on.

WIP of Rammelt's 2019 System Dynamics Review Article which has STELLA and Minsky software versions as supplements. Compare with the older IM-2011 version

Book summary of Albert O Hirschman's 1982 book, explaining cycles of collective public action.

ABOUT THE MODEL

This is a dynamic model that shows the correlation between the health-related policies implemented by the Government in response to COVID-19 outbreak in Burnie, Tasmania, and the policies’ impact on the Economic activity of the area.

 ASSUMPTIONS

The increase in the number of COVID-19 cases is directly proportional to the increase in the Government policies in the infected region. The Government policies negatively impact the economy of Burnie, Tasmania.

INTERESTING INSIGHTS

1. When the borders are closed by the government, the economy is severely affected by the decrease of revenue generated by the Civil aviation/Migration rate. As the number of COVID-19 cases increase, the number of people allowed to enter Australian borders will also decrease by the government. 

2. The Economic activity sharply increases and stays in uniformity. 

3. The death rate drastically decreased as we increased test rate by 90%.

An initial study of the economics of single use coffee pods.

The model is built to demonstrates how Burnie Tasmania can deal with a new COVID-19 outbreaks, taking government policies and economic effects into account.

Any activity  requires the use of energy. Economic activity is not possible without energy,  especially fossil fuels. An increase in economic activity necessarily leads to an increase in the use  fossil fuels and greenhouse gas emissions. In addition there will   be a commensurate increase in waste products, pollution and heat. This is dictated by the laws of physics and unavoidable.  A problem arise when the cost of this degeneration caused by continual economic growth surpasses the benefit society derives from it. The ecological economist Professor Herman Daly (2014) explained that when the impact on the ecosystem is correctly measured, global growth has reached a point where the total private and social costs of economic growth outweigh the private and social benefits. In other words, more economic growth is making global society worse off overall - growth has become uneconomic! The model shows that eventually pressures will build up that counteract the perennial belief that all social ills can be solved with economic growth. 

This is Figure 6 from Lancastle, N. (2012) 'Circuit Theory Extended: The Role of Speculation in Crises' based on Keen, S. (2010). Solving the Paradox of Monetary Profits.

This model demonstrates the intertwining relationship between the economic contribution of industrial logging and that of adventure tourism (dominated by mountain biking).

In terms of the revenue from industrial logging at Derby, it is driven by demand of timber and the timber price. However, the forest resources are limited, which will put constraints on the expansion of industrial logging due to regrowth rate and existing forestation.

The tourism can bring economic benefits to Derby from hospitality and selling tickets to local adventure activities. The hospitality income can be determined by the average length of holidaying at Derby and average local pricing for accommodation, food and beverages and related essentials. Tickets sales are largely affected by the similar factors such as average expense per activity and average number of activities that tourists usually choose. Having explained the streams of possible income from the tourism, the key driver for tourism income is the desire or demand to travel. Unlikely logging, tourism is renewable and perpetual. However, logging can be conceived as a major constraint on attracting as many tourists as the economy so desires.

This is because deforestation caused by logging will diminish the natural scenery at Derby and in turn, the tourist operations and attractions based upon natural scenery. Loss of forest resources is likely to make Derby less attractive to visitors.

In short, the tourism and logging both provides economic benefits to Derby but in a competing relationship. However, the sustainability possessed by tourism cannot be rivaled by industrial logging in long term. Logging revenue reveals its advantage at inception of observed time period. Such advantage wears out over the time due to reduction in resources and sluggish regrowth. Eventually. the tourism income turns into the major player. To understand how they co-exist, please simulate the model. 

Clone of Pesticide Use in Central America for Lab work

Model description:

This model is designed to simulate the outbreak of Covid-19 in Burnie in Tasmania. It also tell us the impact of economic policies on outbreak models and economic growth.

 

Variables:

The simulation takes into account the following variables and its adjusting range: 

 

On the left of the model, the variables are: infection rate( from 0 to 0.25), recovery rate( from 0 to 1), death rate( from 0 to 1), immunity loss rate( from 0 to 1), test rate ( from 0 to 1), which are related to Covid-19.

 

In the middle of the model, the variables are: social distancing( from 0 to 0.018), lock down( from 0 to 0.015), quarantine( from 0 to 0.015), vaccination promotion( from 0 to 0.019), border restriction( from 0 to 0.03), which are related to governmental policies.

 

On the right of the model, the variables are: economic growth rate( from 0 to 0.3), which are related to economic growth.

 

Assumptions:

(1) The model is influenced by various variables and can produce different results. The following values based on the estimation, which differ from actual values in reality.

 

(2) Here are just five government policies that have had an impact on infection rates in epidemic models. On the other hand, these policies will also have an impact on economic growth, which may be positive or negative.

 

(3) Governmental policy will only be applied when reported cases are 10 or more. 

 

(4) This model lists two typical economic activities, namely e-commerce and physical stores. Government policies affect these two types of economic activity separately. They together with economic growth rate have an impact on economic growth.

 

Enlightening insights:

(1) In the first two weeks, the number of susceptible people will be significantly reduced due to the high infection rate, and low recovery rate as well as government policies. The number of susceptible people fall slightly two weeks later. Almost all declines have a fluctuating downward trend.

 

(2) Government policies have clearly controlled the number of deaths, suspected cases and COVID-19 cases.

 

(3) The government's restrictive policies had a negative impact on economic growth, but e-commerce economy, physical stores and economic growth rate all played a positive role in economic growth, which enabled the economy to stay in a relatively stable state during the epidemic.