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

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

 Work Cited   E., Kaplan. "Biomes of the World: Tundra." Alpine Biome. Hong Kong: Marshall Cavendish Corporation., n.d. Web. 23 May 2017.      http://www.blueplanetbiomes.org/tundra.htm
Work Cited


E., Kaplan. "Biomes of the World: Tundra." Alpine Biome. Hong Kong: Marshall Cavendish Corporation., n.d. Web. 23 May 2017.     http://www.blueplanetbiomes.org/tundra.htm
 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 vehic

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)” [1Open 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.pdfOpen 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/
HANDY Model of Societal Collapse from Ecological Economics  Paper   see also D Cunha's model at  IM-15085
HANDY Model of Societal Collapse from Ecological Economics Paper 
see also D Cunha's model at IM-15085
My AP Environmental Homework for the Cats Over Borneo Assignment
My AP Environmental Homework for the Cats Over Borneo Assignment
Two households with PV systems and Electric Vehicles, sharing a battery and connected to the grid. What are the advantages?
Two households with PV systems and Electric Vehicles, sharing a battery and connected to the grid. What are the advantages?


Simple model to illustrate oyster 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]
Simple model to illustrate oyster 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.


Simple model to illustrate Michaelis-Menten equation for nutrient uptake by phytoplankton.  The equation is:  P = Ppot S / (Ks + S)  Where:  P: Nutrient-limited production (e.g. d-1, or mg C m-2 d-1) Ppot: Potential production (same units as P) S: Nutrient concentation (e.g. umol N L-1) Ks: Half sat
Simple model to illustrate Michaelis-Menten equation for nutrient uptake by phytoplankton.

The equation is:

P = Ppot S / (Ks + S)

Where:

P: Nutrient-limited production (e.g. d-1, or mg C m-2 d-1)
Ppot: Potential production (same units as P)
S: Nutrient concentation (e.g. umol N L-1)
Ks: Half saturation constant for nutrient (same units as S)

The model contains no state variables, just illustrates the rate of production, by making the value of S equal to the timestep (in days). Move the slider to the left for more pronounced hyperbolic response, to the right for linear response.
This model is a classic simulation of the production cycle in the ocean, including the effects of the thermocline in switching off advection of dissolved nutrients and detritus to the surface layer.  It illustrates a number of interesting features including the coupling of three state variables in a
This model is a classic simulation of the production cycle in the ocean, including the effects of the thermocline in switching off advection of dissolved nutrients and detritus to the surface layer.

It illustrates a number of interesting features including the coupling of three state variables in a closed cycle, the use of time to control the duration of advection, and the modulus function for cycling annual temperature data over multiple years.

The model state variables are expressed in nitrogen units (mg N m-3), and the calibration is based on:

Baliño, B.M. 1996. Eutrophication of the North Sea, 1980-1990: An evaluation of anthropogenic nutrient inputs using a 2D phytoplankton production model. Dr. scient. thesis, University of Bergen.
 
Fransz, H.G. & Verhagen, J.H.G. 1985. Modelling Research on the Production Cycle of Phytoplankton in the Southern Bight of the Northn Sea in Relation to Riverborne Nutrient Loads. Netherlands Journal of Sea Research 19 (3/4): 241-250.

This model was first implemented in PowerSim some years ago by one of my M.Sc. students, who then went on to become a Buddhist monk. Although this is a very Zen model, as far as I'm aware, the two facts are unrelated.
 The L ogistic Map  is a polynomial mapping (equivalently,  recurrence relation ) of  degree 2 , often cited as an archetypal example of how complex,  chaotic  behaviour can arise from very simple  non-linear  dynamical equations. The map was popularized in a seminal 1976 paper by the biologist  Rob

The Logistic Map is a polynomial mapping (equivalently, recurrence relation) of degree 2, often cited as an archetypal example of how complex, chaotic behaviour can arise from very simple non-linear dynamical equations. The map was popularized in a seminal 1976 paper by the biologist Robert May, in part as a discrete-time demographic model analogous to the logistic equation first created by Pierre François Verhulst

Mathematically, the logistic map is written

where:

 is a number between zero and one, and represents the ratio of existing population to the maximum possible population at year n, and hence x0 represents the initial ratio of population to max. population (at year 0)r is a positive number, and represents a combined rate for reproduction and starvation.
For approximate Continuous Behavior set 'R Base' to a small number like 0.125To generate a bifurcation diagram, set 'r base' to 2 and 'r ramp' to 1
To demonstrate sensitivity to initial conditions, try two runs with 'r base' set to 3 and 'Initial X' of 0.5 and 0.501, then look at first ~20 time steps

The simulation integrates or sums (INTEG) the Nj population, with a change of Delta N in each generation, starting with an initial value of 5. The equation for DeltaN is a version of  Nj+1 = Nj  + mu (1- Nj / Nmax ) Nj  the maximum population is set to be one million, and the growth rate constant mu
The simulation integrates or sums (INTEG) the Nj population, with a change of Delta N in each generation, starting with an initial value of 5.
The equation for DeltaN is a version of 
Nj+1 = Nj  + mu (1- Nj / Nmax ) Nj
the maximum population is set to be one million, and the growth rate constant mu = 3.
 
Nj: is the “number of items” in our current generation.

Delta Nj: is the “change in number of items” as we go from the present generation into the next generation. This is just the number of items born minus the number of items who have died.

mu: is the growth or birth rate parameter, similar to that in the exponential growth and decay model. However, as we extend our model it will no longer be the actual growth rate, but rather just a constant that tends to control the actual growth rate without being directly proportional to it.

F(Nj) = mu(1‐Nj/Nmax): is our model for the effective “growth rate”, a rate that decreases as the number of items approaches the maximum allowed by external factors such as food supply, disease or predation. (You can think of mu as the growth or birth rate in the absence of population pressure from other items.) We write this rate as F(Nj), which is a mathematical way of saying F is affected by the number of items, i.e., “F is a function of Nj”. It combines both growth and all the various environmental constraints on growth into a single function. This is a good approach to modeling; start with something that works (exponential growth) and then modify it incrementally, while still incorporating the working model.

Nj+1 = Nj + Delta Nj : This is a mathematical way to say, “The new number of items equals the old number of items plus the change in number of items”.

Nj/Nmax: is what fraction a population has reached of the maximum "carrying capacity" allowed by the external environment. We use this fraction to change the overall growth rate of the population. In the real world, as well as in our model, it is possible for a population to be greater than the maximum population (which is usually an average of many years), at least for a short period of time. This means that we can expect fluctuations in which Nj/Nmax is greater than 1.

This equation is a form of what is known as the logistic map or equation. It is a map because it "maps'' the population in one year into the population of the next year. It is "logistic'' in the military sense of supplying a population with its needs. It a nonlinear equation because it contains a term proportional to Nj^2 and not just Nj. The logistic map equation is also an example of discrete mathematics. It is discrete because the time variable j assumes just integer values, and consequently the variables Nj+1 and Nj do not change continuously into each other, as would a function N(t). In addition to the variables Nj and j, the equation also contains the two parameters mu, the growth rate, and Nmax, the maximum population. You can think of these as "constants'' whose values are determined from external sources and remain fixed as one year of items gets mapped into the next year. However, as part of viewing the computer as a laboratory in which to experiment, and as part of the scientific process, you should vary the parameters in order to explore how the model reacts to changes in them.
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
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
 The World3 model is a detailed simulation of human population growth from 1900 into the future. It includes many environmental and demographic factors.  
 Use the sliders to experiment with the initial amount of non-renewable resources to see how these affect the simulation. Does increasing the amo

The World3 model is a detailed simulation of human population growth from 1900 into the future. It includes many environmental and demographic factors.


Use the sliders to experiment with the initial amount of non-renewable resources to see how these affect the simulation. Does increasing the amount of non-renewable resources (which could occur through the development of better exploration technologies) improve our future? Also, experiment with the start date of a low birth-rate, environmentally focused policy.

Fertilizer inflow can cause lake eutrophication. In this simulation, we are studying what happens in a simple lake ecosystem.
Fertilizer inflow can cause lake eutrophication. In this simulation, we are studying what happens in a simple lake ecosystem.
Simulation of MTBF with controls   F(t) = 1 - e ^ -λt   Where    • F(t) is the probability of failure    • λ is the failure rate in 1/time unit (1/h, for example)   • t is the observed service life (h, for example)  The inverse curve is the trust time On the right the increase in failures brings its
Simulation of MTBF with controls

F(t) = 1 - e ^ -λt 
Where  
• F(t) is the probability of failure  
• λ is the failure rate in 1/time unit (1/h, for example) 
• t is the observed service life (h, for example)

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
If we follow the slope from the leftmost start to where it begins to flatten out this can be considered the first period. The first period is characterized by a decreasing failure rate. It is what occurs during the “early life” of a population of units. The weaker units fail leaving a population that is more rigorous.

Useful Life
The next period is the flat bottom portion of the graph. It is called the “useful life” period. Failures occur more in a random sequence during this time. It is difficult to predict which failure mode will occur, but the rate of failures is predictable. Notice the constant slope.  

Wearout
The third period begins at the point where the slope begins to increase and extends to the rightmost end of the graph. This is what happens when units become old and begin to fail at an increasing rate. It is called the “wearout” period. 
	This a simple and "totally accurate" model of the exponential human population.
This a simple and "totally accurate" model of the exponential human population.
all pictures sourced from google images
all pictures sourced from google images

Clone of Pesticide Use in Central America for Lab work        This model is an attempt to simulate what is commonly referred to as the “pesticide treadmill” in agriculture and how it played out in the cotton industry in Central America after the Second World War until around the 1990s.     The cotto
Clone of Pesticide Use in Central America for Lab work


This model is an attempt to simulate what is commonly referred to as the “pesticide treadmill” in agriculture and how it played out in the cotton industry in Central America after the Second World War until around the 1990s.

The cotton industry expanded dramatically in Central America after WW2, increasing from 20,000 hectares to 463,000 in the late 1970s. This expansion was accompanied by a huge increase in industrial pesticide application which would eventually become the downfall of the industry.

The primary pest for cotton production, bol weevil, became increasingly resistant to chemical pesticides as they were applied each year. The application of pesticides also caused new pests to appear, such as leafworms, cotton aphids and whitefly, which in turn further fuelled increased application of pesticides. 

The treadmill resulted in massive increases in pesticide applications: in the early years they were only applied a few times per season, but this application rose to up to 40 applications per season by the 1970s; accounting for over 50% of the costs of production in some regions. 

The skyrocketing costs associated with increasing pesticide use were one of the key factors that led to the dramatic decline of the cotton industry in Central America: decreasing from its peak in the 1970s to less than 100,000 hectares in the 1990s. “In its wake, economic ruin and environmental devastation were left” as once thriving towns became ghost towns, and once fertile soils were wasted, eroded and abandoned (Lappe, 1998). 

Sources: Douglas L. Murray (1994), Cultivating Crisis: The Human Cost of Pesticides in Latin America, pp35-41; Francis Moore Lappe et al (1998), World Hunger: 12 Myths, 2nd Edition, pp54-55.

 A simulation illustrating simple predator prey dynamics. You have two populations.

A simulation illustrating simple predator prey dynamics. You have two populations.

 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

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 model uses simple functions (converters, cosine) to simulate the water balance inside a reservoir.
This model uses simple functions (converters, cosine) to simulate the water balance inside a reservoir.