As initially proposed by Pr. William M White of Cornell University:

This model describes the flow of energy from generation to consumption for neighborhoods in the metro Atlanta area. It also calculates the cost of energy production and the number of years it will take to recover that cost.

Simulation of MTBF with controls


The inverse curve is the trust time
On the right the increase in failures brings its inverse which is loss of trust and move into suspicion and lack of confidence.
This can be seen in strategic social applications with those who put economy before providing the priorities of the basic living infrastructures for all.

This applies to policies and strategic decisions as well as physical equipment.
A) Equipment wears out through friction and preventive maintenance can increase the useful lifetime, 
B) Policies/working practices/guidelines have to be updated to reflect changes in the external environment and eventually be replaced when for instance a population rises too large (constitutional changes are required to keep pace with evolution, e.g. the concepts of the ancient Greeks, 3000 years ago, who based their thoughts on a small population cannot be applied in 2013 except where populations can be contained into productive working communities with balanced profit and loss centers to ensure sustainability)

Early Life
Useful Life

Wearout

School assessment

WIP Stock Flow representation of Panarchy Adaptive Cycles

This is a model for the mass flow of phosphorus in a stream called "Ljurabäck" in Norrköping during two months. The stream flows from a lake called "Glan" to a large stream called "Motala Ström". 

This model shows the cycling of Mercury within a coastal wetland system. This cycling shows Elemental Mercury, Hg 2+, and Methylmercury within the soil, water, and air, and also interaction with the plants in the system.

My model is on global population and its impact on the availability of natural resources. The stocks in my system include food availability, soil resources and water resource availability. One question I believe my model can address is, what are the connections between food availability, soil resources and water resource availability; or in other words, are these stocks influenced equally by variables?  I hope to show a direct correlation between all three of these stocks. Food availability as stated in The Impact of Population Growth on Food Supplies and the Environment stated that, “The continued production of an adequate food supply is directly dependent on ample fertile land, fresh water, energy, plus the maintenance of biodiversity.” As population continues to grow so will the inputs to natural resources including water, fertilizer, and the need to have more available land.  What is more astonishing is that if these natural resources are never completely tapped dry, on a per capita perspective availability these resources will decline on astronomical levels since it has to be split amongst people (Pimental et al, 1996).

The flows in my system include food production,drought, water pollution, and greenhouse gases. I picked drought as a flow since it directly impacts the level of water available. Take for instance in California, the five year drought has caused scarcity and triggered state-wide executive orders to conserve water (California Department of Water Resources, 2017). Drought and water pollution can be affected by the number of people living in a country, which is why I picked these elements as flows. Furthermore food production, water pollution and greenhouse gases have strong influences on the availability of natural resources.

I picked mortality rates, birth rates, water scarcity, and industrial development as my variables. Since birth rates and mortality rates vary depending on the country I picked these as variables on my system since population growth is influenced by these variables.   Impact of Population Growth describes how the U. S. is already being affected by population growth, as stated here, “In populous industrial nations such as the United States, most economies of scale are already being exploited; we are on the diminishing returns part of most of the important curves.”

I have decided to change “developed countries” and undeveloped countries” as stocks to variables since these factors actually act more like variables. One question I hope to address with my model is how developed countries can  reduce their impact on resources? Furthermore, Population growth rate does depend on whether a country is developed versus undeveloped, so a country's level of economic development is more of a variable. I have decided to change food production from a stock to a flow, since it seems to be more of a flow that might affect the level of a stock of available food. I have also changed water scarcity from a stock to a variable because it actually affects the flow of water into an overall stock of fresh drinking water

Economic growth cannot go on forever, although politicians and most economist seem to think so. The activity involved in economic growth necessarily  generates entropy (disorder and environmental degradation). Entorpy in turn generates powerful negative feedback loops which will, as a response from nature, ensure that economic activity will eventually grind to a complete halt.  In these circumstances organised society cannot persist and will collapse. The negative feedback loops shown in this graph have already started to operate. The longer economic growth continues unabated, the more powerful these negative feedback loops will become. How long can economic growth continue before it is overwhelmed? It may not be very far in the future.

this is the Australian food web of the water buffalo

A tutorial on the basics of insightmaker

This model adresses the primary production for phytoplankton growth, based on Steele’s light intensity equation and Michaelis-Menten equation for nutrient limitation.

In 2012, the City of Vancouver created a sustainability strategy for staying on the leading edge of urban development called the “Greenest City: 2020 Action Plan (GCAP)” [1—Open in Pop-up]. In the report, the GCAP noted that its highest priority action was to encourage the use of electric vehicle transport in both public and private sectors. Since then, programs such as the Clean Energy Vehicle (CEV) program have been revamped to encourage consumers to choose the greener choice, often rewarding owners with up to $5000 in incentives for battery-powered vehicles and plug-in hybrids. However, the benefits of choosing electric cars are not all clear as several reports have found that hybrid electric vehicles (HEV), plug-in electric hybrid vehicles (PHEV), and battery electric cars (BEV) generate more carbon emissions during their production than current conventional vehicles [2]. I thought it would be interesting to study this sustainability issue through a systems model to determine how much impact it has on the environment compared to conventional vehicles. 

https://insightmaker.com/insight/159243/CO2-Emissions-by-Vehicle-Type-Gasoline-vs-Electric

Our model explores both carbon emissions of standard gasoline vehicles and electric vehicles from production to distribution in Canada specifically. Unfortunately, we were unable to find any statistics regarding the number of electric vehicles in production in Canada, so we have used the sales number as our production number estimate. For CO2 emission statistics, we made sure to carefully separate different types of electric vehicles as the production of the battery in battery electric vehicles have significantly more carbon emissions during production.

As expected, the carbon emissions from electric vehicles are much lower than those of gasoline vehicles after taking into account the lifecycle emissions from an average lifespan of 8 years on the road (which is the standard warranty length offered from most car companies). Some interesting things to note are that with our current rise in electric vehicle adoption, electric vehicles will dominate the roads in about 100 years. This transformation may be further accelerated by the large-scale initiatives offered by governmental organizations and increased awareness for sustainable practices. Furthermore, it was very surprising to find that electric vehicle carbon emissions will exceed that of gasoline vehicles after nearly 1000 years, but after further analysis, this makes sense as by then electric vehicles will greatly outnumber gasoline vehicles. This means that electric vehicles are not only the greener choice -- electric vehicles are by far the greenest choice as it will take nearly a thousand years before its emissions will be equal to that of its gasoline counterpart. In fact, it may even take longer than 1000 years for electric vehicles to emit more carbon emissions than gasoline vehicles if we continue looking for more sustainable methods for producing electricity and proactively choose renewable energy over fossil fuels.

Sources:

[1] https://vancouver.ca/files/cov/Greenest-city-action-plan.pdf—Open in Pop-up

[2] http://www.ccsenet.org/journal/index.php/jsd/article/view/64183

Statistics for number of gasoline and electric vehicle sales:

Gasoline Vehicles: https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=2010000201

Electric Vehicles: https://www.fleetcarma.com/electric-vehicle-sales-canada-2017/

A system diagram for the Mojave Desert for an assignment at OSU- RNG 341.

This diagram provides an accessible description of the key processes that guide the water quality within a lake.

Fig 3.1 from Jorgen Randers book 2052 a Global Forecast for the Next Forty Years

Here is the Covid 19 Statistics model based on the Philippines.

This is a causal diagram story I made as an introduction to a workshop on systems thinking.

Eastern oyster growth model calibrated for Long Island Sound

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)

Simple model to illustrate   algal  ,   growth based on primary production of Phytoplankton as a state variable, forced by light and nutrients, running for a yearly period.

Phytoplankton growth based on on Steele's and Michaelis-Menten equations), where: 

Primary Production=(([Pmax]*[I]/[Iopt]*exp(1-[I]/[Iopt])*[S])/([Ks]+[S]))

Pmax: Maximum production (d-1)
I: Light energy at depth of interest (uE m-2 s-1)
Iopt: Light energy at which Pmax occurs (uE m-2 s-1)
S: Nutrient concentration (umol N L-1)
Ks: Half saturation constant for nutrient (umol N L-1).

Further developments:
- Nutrients as state variable in cycle with detritus from phytoplankton and oyster biomass.
- Light limited by the concentration of phytoplankton.
- Temperature effect on phytoplankton and Oyster growth.

  Biogas, model  as well birefineray option to seperate c02 , chp from bogas model are proposed

My AP Environmental Homework for the Cats Over Borneo Assignment