INDUSTRIAL AGRICULTURE IMPACT ON POLLUTION AND RESOURCES  THE 2017 MODEL (BY GUY LAKEMAN) EMPHASIZES THE PEAK IN POLLUTION BEING CREATED BY OVERPOPULATION WITH THE CARRYING CAPACITY OF ARABLE LAND NOW BEING 1.5 TIMES OVER A SUSTAINABLE FUTURE (PASSED IN 1990) AND NOW INCREASING IN LOSS OF HUMA
Flood Engineering Ocean Strategy Yield Regeneration Pollution Tornado Lifetime Rural Demographics Industrial Urban Environment Labor Jobs Population Growth Population Cyclone Agriculture Food Technology Land Resources Sea Fish Plants Animals Weather Transport Climate Failure Death Economics Politics Mortality Fertility Typhoon Hurricane Silicene Energy Science Loss Drought Nuclear Graphene Solar Resource Health Services

INDUSTRIAL AGRICULTURE IMPACT ON POLLUTION AND RESOURCES THE 2017 MODEL (BY GUY LAKEMAN) EMPHASIZES THE PEAK IN POLLUTION BEING CREATED BY OVERPOPULATION WITH THE CARRYING CAPACITY OF ARABLE LAND NOW BEING 1.5 TIMES OVER A SUSTAINABLE FUTURE (PASSED IN 1990) AND NOW INCREASING IN LOSS OF HUMAN SUSTAINABILITY DUE TO SEA RISE AND EXTREME GLOBAL WATER RELOCATION IN WEATHER CHANGES IN FLOODS AND DROUGHTS AND EXTENDED TROPICAL AND HORSE LATTITUDE CYCLONE ACTIVITY AROUND HADLEY CELLS

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

THIS MODEL BY GUY LAKEMAN, FROM METRICS OBTAINED USING A MORE COMPREHENSIVE VENSIM SOFTWARE MODEL, SHOWS CURRENT CONDITIONS CREATED BY THE LATEST WEATHER EXTREMES AND LOSS OF ARABLE LAND BY THE  ALBEDO EFECT MELTING THE POLAR CAPS TOGETHER WITH NORTHERN JETSTREAM SHIFT NORTHWARDS, AND A NECESSITY TO ACT BEFORE THERE IS HUGE SUFFERING.
BY SETTING THE NEW ECOLOGICAL POLICIES TO 2015 WE CAN SEE THAT SOME POPULATIONS CAN BE SAVED BUT CITIES WILL SUFFER MOST. 
CURRENT MARKET SATURATION PLATEAU OF SOLID PRODUCTS AND BEHAVIORAL SINK FACTORS ARE ALSO ADDED

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.

INDUSTRIAL AGRICULTURE IMPACT ON POLLUTION AND RESOURCES
Guy Lakeman
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
TURBULENCE Economics Environment MATHS BIFURCATION DECAY Mathematics Model Finance Science Engineering STABILITY SUSTAINABLE Fractals Growth Population Chaos Strategy Demographics
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.
Clone of POPULATION LOGISTIC MAP (WITH FEEDBACK)
Roman Knaus
Simulação de processo de  produção de Bio Diesel O biodiesel pode ser produzido a partir de fontes renováveis como óleos e gorduras animais e vegetais. A Medida Provisória n° 214, de 13 de setembro de 2004, define o biodiesel como um combustível para motores a combustão interna com ignição p
Engineering
Simulação de processo de  produção de Bio Diesel

O biodiesel pode ser produzido a partir de fontes renováveis como óleos e gorduras animais e vegetais. A Medida Provisória n° 214, de 13 de setembro de 2004, define o biodiesel como um combustível para motores a combustão interna com ignição por compressão, renovável e biodegradável, derivado de óleos vegetais ou de gorduras animais, que possa substituir parcial ou totalmente o óleo diesel de origem fóssil.
Sistema integrado de tecnologia do Bio Diesel
alunodoisgtp EngPI
5
EFB Calculation of energy from wastes
Engineering
EFB Calculation of energy from wastes
Clone of Clone of Gasification of wastesEFB
Aluno 08 gte
Mass and Heat Balance for POME
Engineering
Mass and Heat Balance for POME
Mass And Heat Balance for POME
Victor GPS
6
o projeto de  eco cidade sustentavel o projeto biogas , espirulina  alga Mass & Energy balance in full scale plants for food waste AD  http://www.valorgas.soton.ac.uk/Pub_docs/IWA%20Vienna%202011/Presentation%203%20Bolzonella%20Mass%20&%20Energy%20balance%20VALORGAS%20ADSWEC%20V
Engineering
o projeto de  eco cidade sustentavel
o projeto biogas , espirulina  alga


Mass & Energy balance in full scale plants for food waste AD 

1. Simulacao de proceso de  producao BioD
 
 2. Simulacao de proceso de  producao  biohidogenio

 3 .Simulacao de proceso de  producao Biogas

Clone of Sistema integrado de tecnologia Biogas, cidade ecologica e Economia Verde
Aluno Seven
Practice from meadows reading
Engineering
Practice from meadows reading
Stabilizing Feedback Loop
Isabelle Vernon
THE 2017 MODEL (BY GUY LAKEMAN) EMPHASIZES THE PEAK IN POLLUTION BEING CREATED BY OVERPOPULATION WITH THE CARRYING CAPACITY OF ARABLE LAND NOW BEING 1.5 TIMES OVER A SUSTAINABLE FUTURE (PASSED IN 1990) AND NOW INCREASING IN LOSS OF HUMAN SUSTAINABILITY DUE TO SEA RISE AND EXTREME GLOBAL WATER REL
Services Environment Demographics Population Growth Population Weather Climate Failure Death Mortality Science Technology Engineering Strategy Economics Politics Fertility Health Resources Land Jobs Labor Urban Industrial Rural Lifetime Pollution Regeneration Yield Ocean Sea Fish Plants Animals Flood Drought Loss Hurricane Typhoon Tornado Cyclone Agriculture Food Energy Nuclear Solar Resource Graphene Silicene Transport

THE 2017 MODEL (BY GUY LAKEMAN) EMPHASIZES THE PEAK IN POLLUTION BEING CREATED BY OVERPOPULATION WITH THE CARRYING CAPACITY OF ARABLE LAND NOW BEING 1.5 TIMES OVER A SUSTAINABLE FUTURE (PASSED IN 1990) AND NOW INCREASING IN LOSS OF HUMAN SUSTAINABILITY DUE TO SEA RISE AND EXTREME GLOBAL WATER RELOCATION IN WEATHER CHANGES IN FLOODS AND DROUGHTS AND EXTENDED TROPICAL AND HORSE LATTITUDE CYCLONE ACTIVITY AROUND HADLEY CELLS

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

THIS MODEL BY GUY LAKEMAN, FROM METRICS OBTAINED USING A MORE COMPREHENSIVE VENSIM SOFTWARE MODEL, SHOWS CURRENT CONDITIONS CREATED BY THE LATEST WEATHER EXTREMES AND LOSS OF ARABLE LAND BY THE  ALBEDO EFECT MELTING THE POLAR CAPS TOGETHER WITH NORTHERN JETSTREAM SHIFT NORTHWARDS, AND A NECESSITY TO ACT BEFORE THERE IS HUGE SUFFERING.
BY SETTING THE NEW ECOLOGICAL POLICIES TO 2015 WE CAN SEE THAT SOME POPULATIONS CAN BE SAVED BUT CITIES WILL SUFFER MOST. 
CURRENT MARKET SATURATION PLATEAU OF SOLID PRODUCTS AND BEHAVIORAL SINK FACTORS ARE ALSO ADDED

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.

Clone of 2017 Weather & Climate Extreme Loss of Arable Land and Ocean Fertility by Guy Lakeman - The World3+ Model: Forecaster
Rhys Almario
Simulação de processo de  produção de Bio Diesel O biodiesel pode ser produzido a partir de fontes renováveis como óleos e gorduras animais e vegetais. A Medida Provisória n° 214, de 13 de setembro de 2004, define o biodiesel como um combustível para motores a combustão interna com ignição p
Engineering
Simulação de processo de  produção de Bio Diesel

O biodiesel pode ser produzido a partir de fontes renováveis como óleos e gorduras animais e vegetais. A Medida Provisória n° 214, de 13 de setembro de 2004, define o biodiesel como um combustível para motores a combustão interna com ignição por compressão, renovável e biodegradável, derivado de óleos vegetais ou de gorduras animais, que possa substituir parcial ou totalmente o óleo diesel de origem fóssil.
Clone of Sistema integrado de tecnologia do Bio Diesel
alunodoisgtp EngPI
4
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
Science SUSTAINABLE Demographics Strategy STABILITY DECAY Environment Growth MATHS Population Mathematics Chaos Fractals BIFURCATION TURBULENCE Economics Finance Model Engineering
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.
Clone of POPULATION LOGISTIC MAP (WITH FEEDBACK)
Shrishail
A causal loop diagram for a Modified Centralized Water System.
Engineering
A causal loop diagram for a Modified Centralized Water System.
Causal Loop Diagram for Modified Centralized Water Treatment System
Danny
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
Science Strategy Failure Risk MTBF BIFURCATIONS Growth Population Demographics Navier Stokes Engineering TURBULENCE Chaos Fractals Health Biology Physics Mathematics Finance Economics Environment
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. 
Clone of Clone of BATHTUB MEAN TIME BETWEEN FAILURE (MTBF) RISK
Ivan Stamenkovic
A model to gain understanding of the causes and effects of a population's interest in engineering.
Interest Engineering Economy
A model to gain understanding of the causes and effects of a population's interest in engineering.
Clone of Public interest in engineering
Cheryl
A tool designed to remind us about the audience in a systemic way.  Small groups would collectively distribute six sigma projects on the converters to understand the current state in an objective manner. The intent of the model merely collecting evidence, students from within their communities s
Data Science Urban Planning Simulations Engineering
A tool designed to remind us about the audience in a systemic way. 

Small groups would collectively distribute six sigma projects on the converters to understand the current state in an objective manner.

The intent of the model merely collecting evidence, students from within their communities serve as a new role to bridge the trust and rebuild hope with direct engagement. 

Thematic subjects from the SDG's
Economic, Justice, Health, nutrition and the poverty equation are segmented by three income or economic groupings.  The grouping in this manner enables us to identify patterns which are true in all parts of the world.   (80/20 rule)



Clone of People with Healthcare in any population - Disabled Determination
Guilherme Werpel Fernandes
Tyson Browning (2009) ​The Many Views of a Process:Toward a  Process  Architecture Framework for Product Development Systems Engineering: paper
Process Engineering Framework
Tyson Browning (2009) ​The Many Views of a Process:Toward a Process Architecture Framework for Product Development Systems Engineering: paper
Clone of Process Architecture Framework
Carl Gomersall
Addition of an acceptance test which discovers rework (Cooper et al.)
Management Engineering Project

Addition of an acceptance test which discovers rework (Cooper et al.)

Clone of Clone of Project management 102
George S.
Tyson Browning (2009) ​The Many Views of a Process:Toward a  Process  Architecture Framework for Product Development Systems Engineering: paper
Process Engineering Framework
Tyson Browning (2009) ​The Many Views of a Process:Toward a Process Architecture Framework for Product Development Systems Engineering: paper
Clone of Process Architecture Framework
Nilo Guimaraes
Addition of an acceptance test which discovers rework (Cooper et al.) plus introduction of new tasks and tipping point (Taylor and Ford). Here schedule pressure producing overtime is also added
Project Management Engineering

Addition of an acceptance test which discovers rework (Cooper et al.) plus introduction of new tasks and tipping point (Taylor and Ford). Here schedule pressure producing overtime is also added

Clone of Project management 104
Sam Russell
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
Chaos Environment Economics Strategy Failure Risk MTBF BIFURCATIONS Finance Growth Mathematics Population Demographics Science Physics Biology Navier Stokes Engineering Health TURBULENCE Fractals
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. 
Clone of BATHTUB MEAN TIME BETWEEN FAILURE (MTBF) RISK
atif
Addition of an acceptance test which discovers rework (Cooper et al.)
Project Management Engineering

Addition of an acceptance test which discovers rework (Cooper et al.)

Clone of Project management 102
Raquel Martin
THE BROKEN LINK BETWEEN SUPPLY AND DEMAND CREATES TURBULENT CHAOTIC DESTRUCTION The existing global capitalistic growth paradigm is totally flawed Growth in supply and productivity is a summation of variables as is demand ... when the link between them is broken by catastrophic failure in a compon
Physics Mathematics Navier Stokes Economics Environment Demand Strategy Finance Engineering TURBULENCE Chaos Fractals Supply Health Biology
THE BROKEN LINK BETWEEN SUPPLY AND DEMAND CREATES TURBULENT CHAOTIC DESTRUCTION

The existing global capitalistic growth paradigm is totally flawed

Growth in supply and productivity is a summation of variables as is demand ... when the link between them is broken by catastrophic failure in a component the creation of unpredictable chaotic turbulence puts the controls ito a situation that will never return the system to its initial conditions as it is STIC system (Lorenz)

The chaotic turbulence is the result of the concept of infinite bigness this has been the destructive influence on all empires and now shown up by Feigenbaum numbers and Dunbar numbers for neural netwoirks

See Guy Lakeman Bubble Theory for more details on keeping systems within finite working containers (villages communities)

Clone of THE BROKEN LINK BETWEEN SUPPLY AND DEMAND CREATES CHAOTIC TURBULENCE (+controls)
Charles Møller
Clusters of interacting methods for improving health services network design and delivery. Includes Forrester quotes on statistical vs SD methods and the Modeller's dilemma. Simplified version of IM-14982  combined with  IM-17598  and  IM-9773
Platform Hybrid Health Care Data Science Systems Technology ABM Engineering Methods Forrester Complexity EHealth Decision Making
Clusters of interacting methods for improving health services network design and delivery. Includes Forrester quotes on statistical vs SD methods and the Modeller's dilemma. Simplified version of IM-14982 combined with IM-17598 and IM-9773
Clone of Complex Decision Technologies
KEVIN LEWIS O'CARROLL
o projeto de  eco cidade sustentavel o projeto biogas , espirulina  algae 1. Simulacao de proceso de  producao BioD    2. Simulacao de proceso de  producao  biohidogenio  3 .Simulacao de proceso de  producao Biogas 4. Simulacao de Estacao de tratamento  Efluente. 5. Simulacao de proce
Engineering
o projeto de  eco cidade sustentavel
o projeto biogas , espirulina  algae
1. Simulacao de proceso de  producao BioD
 
 2. Simulacao de proceso de  producao  biohidogenio

 3 .Simulacao de proceso de  producao Biogas

4. Simulacao de Estacao de tratamento  Efluente.

5. Simulacao de proceso de  producao Algae 
Sistema integrado de tecnologia de processo BioD e Economia Verde
Pagandai V Pannir Selvam
14
THE 2017 MODEL (BY GUY LAKEMAN) EMPHASIZES THE PEAK IN POLLUTION BEING CREATED BY OVERPOPULATION WITH THE CARRYING CAPACITY OF ARABLE LAND NOW BEING 1.5 TIMES OVER A SUSTAINABLE FUTURE (PASSED IN 1990) AND NOW INCREASING IN LOSS OF HUMAN SUSTAINABILITY DUE TO SEA RISE AND EXTREME GLOBAL WATER REL
Mortality Transport Silicene Graphene Resource Solar Nuclear Energy Food Agriculture Cyclone Tornado Typhoon Hurricane Loss Drought Flood Animals Plants Fish Sea Ocean Yield Regeneration Pollution Lifetime Rural Industrial Urban Labor Jobs Land Resources Services Health Fertility Politics Economics Strategy Engineering Technology Science Death Failure Climate Weather Population Population Growth Demographics Environment

THE 2017 MODEL (BY GUY LAKEMAN) EMPHASIZES THE PEAK IN POLLUTION BEING CREATED BY OVERPOPULATION WITH THE CARRYING CAPACITY OF ARABLE LAND NOW BEING 1.5 TIMES OVER A SUSTAINABLE FUTURE (PASSED IN 1990) AND NOW INCREASING IN LOSS OF HUMAN SUSTAINABILITY DUE TO SEA RISE AND EXTREME GLOBAL WATER RELOCATION IN WEATHER CHANGES IN FLOODS AND DROUGHTS AND EXTENDED TROPICAL AND HORSE LATTITUDE CYCLONE ACTIVITY AROUND HADLEY CELLS

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

THIS MODEL BY GUY LAKEMAN, FROM METRICS OBTAINED USING A MORE COMPREHENSIVE VENSIM SOFTWARE MODEL, SHOWS CURRENT CONDITIONS CREATED BY THE LATEST WEATHER EXTREMES AND LOSS OF ARABLE LAND BY THE  ALBEDO EFECT MELTING THE POLAR CAPS TOGETHER WITH NORTHERN JETSTREAM SHIFT NORTHWARDS, AND A NECESSITY TO ACT BEFORE THERE IS HUGE SUFFERING.
BY SETTING THE NEW ECOLOGICAL POLICIES TO 2015 WE CAN SEE THAT SOME POPULATIONS CAN BE SAVED BUT CITIES WILL SUFFER MOST. 
CURRENT MARKET SATURATION PLATEAU OF SOLID PRODUCTS AND BEHAVIORAL SINK FACTORS ARE ALSO ADDED

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.

Clone of 2017 Weather & Climate Extreme Loss of Arable Land and Ocean Fertility by Guy Lakeman - The World3+ Model: Forecaster
Juan Pablo Velazquez Gaytan