This model is an attempt to understand the interactions within an economy in an attempt to determine where the leverage points are to stimulate an economy.

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The statement that there can be no economic activity without  energy and that fossil fuels are finite contrasts with the fact that money is not finite and can be created by governments via their central banks at zero marginal cost whenever needed.

An important fact about COAL, GAS and OIL (even when produced via fracking) is that their net energy ratios are falling rapidly. In other words the energy needed to extract a given quantity of fossil fuels is constantly increasing. This ratio (Energy Invested on Energy Returned - EIOER) provides yet another warning that we can no longer rely on fossil fuels to power our economies. We cannot wait until the ratio falls to 1/1 before we invest seriously in alternative sources of energy, because by then industrial society as we know it doday will have ceased to exist. 

PS: A link between growth in energy consumption and GDP growth is clearly illustrated on slide 13 of Gail Tverberg's presentaion entitled ''Oops! The world economy depends on an energy-related bubble''. In fact, the slide shows that growth in energy consumption usually precedes GDP growth.

https://gailtheactuary.files.wordpress.com/2015/10/oops-debt-bubble-10_30_15.pdf

Scratch build of a stock-flow consistent model of a closed economy, based on a current transactions matrix

The statement that there can be no economic activity without  energy and that fossil fuels are finite contrasts with the fact that money is not finite and can be created by governments via their central banks at zero marginal cost whenever needed.

An important fact about COAL, GAS and OIL (even when produced via fracking) is that their net energy ratios are falling rapidly. In other words the energy needed to extract a given quantity of fossil fuels is constantly increasing. This ratio (Energy Invested on Energy Returned - EIOER) provides yet another warning that we can no longer rely on fossil fuels to power our economies. We cannot wait until the ratio falls to 1/1 before we invest seriously in alternative sources of energy, because by then industrial society as we know it doday will have ceased to exist. 

PS: A link between growth in energy consumption and GDP growth is clearly illustrated on slide 13 of Gail Tverberg's presentaion entitled ''Oops! The world economy depends on an energy-related bubble''. In fact, the slide shows that growth in energy consumption usually precedes GDP growth.

https://gailtheactuary.files.wordpress.com/2015/10/oops-debt-bubble-10_30_15.pdf

This is the original model version (v1.0) with default "standard run" parameter set: see detailed commentary here and here. As of 2 September 2015, ongoing development has now shifted to this version of the model.

The significance of reduced energy return on energy invested (EROI) in the transition from fossil fuel to renewable primary energy sources is often disputed by both renewable energy proponents and mainstream economists.​ This model illustrates the impact of EROI in large-scale energy transition using a system dynamics approach. The variables of primary interest here are: 1) net energy available to "the rest of the economy" as renewable penetration increases [Total final energy services out to the economy]; and 2) the size of the energy sector as a proportion of overall economic activity, treating energy use as a very rough proxy for size [Energy services ratio].
This model aggregates energy supply in the form of fuels and electricity as a single variable, total final energy services, and treats the global economy as a single closed system.
The model includes all major incumbent energy sources, and assumes a transition to wind, PV, hydro and nuclear generated electricity, plus biomass electricity and fuels. Hydro, biomass and nuclear growth rates are built into the model from the outset, and wind and PV emplacement rates respond to the built-in retirement rates for fossil energy sources, by attempting to make up the difference between the historical maximum total energy services out to the global economy, and the current total energy services out. Intermittency of PV and wind are compensated via Li-ion battery storage. Note, however, that seasonal variation of PV is not fully addressed i.e. PV is modeled using annual and global average parameters. For this to have anything close to real world validity, this would require that all PV capacity is located in highly favourable locations in terms of annual average insolation, and that energy is distributed from these regions to points of end use. The necessary distribution infrastructure is not included in the model at this stage.
It is possible to explore the effect of seasonal variation with PV assumed to be distributed more widely by de-rating capacity factor and increasing the autonomy period for storage.

This version of the model takes values for emplaced capacities of conventional sources (i.e. all energy sources except wind and PV) as exogenous inputs, based on data generated from earlier endogenously-generated emplaced capacities (for which emplacement rates as a proportion of existing installed capacity were the primary exogenous input).

'Efficiencyism’  can be described as a blind belief in the effectiveness of efficiency measures without taking into account circumstances and the wider context.   The graph on the left shows how the frequent use of the term 'efficiency' at the level of local interactons can lead to the emergence of  'efficiencyism' through upward causation, denoted by the arrows pointing upwards.  However, there is also downward causation from the global level depicted by the red arrows which can increase the blind application of efficiency measures at the local level. In other words, efficiency for the sake of efficiency becomes a dominant idea.  The tyrannical influence of 'eficiencyism' affects all of us to varying degrees and unfortunately can often have very negative side effects, such as an increase in unemployment, social injustice and even increase inequality.  Of  course, well thought out efficiency improvements can also bring great  benefits.   I recommend reading an excellent article by Dr. Charles Chandler, who explains the term 'efficiencyism' with some excellent examples and also points to some  of its undesirable effects.

http://www.ageofoe.com/010-efficiencyism-holds-us-back/

Simple mock-up model of how prioritizing various push-pull factors impacts the size of the immigrant population over time as well as economic benefits to the U.S. economy.

Model showing the effect of bank lending of deposited money as a multiplier in the creation of new money. Multiplier effect is shown as related to the bank reserve requirement on deposited funds.

This is a model that will simulate a medieval fantasy population with regular trades

A model which simulates the competition between logging versus adventure tourism (mountain bike ridding) in Derby Tasmania.  Simulation borrowed from the Easter Island simulation.

The systemic problem is to understand what influence the gold price?

Many articles say that the gold price is manipulated and some analysts predict that the bubble will burst. (1)

We think that understanding how gold can be influenced by different factors is an interesting research topic. The variation of the gold price is a real-world problem which evaluates through the interaction of a group of different elements.

It seems that the gold price is a very complex problem understanding. Of course everybody has his own thinking about the problem according to his own filter.

But this approach is most of the time not valuable because there is not a full view of all the variables and their link. In a context of a growing demand and a constant supply, be able to determine if gold price will continue to increase and if this asset will represent a safe investment for the new decade.

In September 2011, gold price surged a record, $1,274,75 an ounce. According to the Commodities guru George Soros “gold was the ultimate bubble" and was no longer a safe investment.

On the other hand, the research conducts by metal consultant GFMS predicted that gold will hit a new record of $1,300 an ounce. (2)

Who was right? Both of them. 

This example illustrates how complex is the problem.

At the time of this research the price of gold is $1,316,79 an ounce.

Wealthy persons are concerned by preserving their fortune, they also look to maximise their wealth and to keep it safe. Many options are available to investors, despite buillion is a popular asset on a long-term portfolio, nowadays is it gold a safe investment? That is a good question. Also understanding the impact of gold on the economy and how it is link to poverty might be interesting. To analyze an issue, one must first define it.

In order to get a better understanding of the gold price we will model this complex problem. Our goal is to visualize the interconnection of elements and be able to identify feedback loops with the aim to understand the complexity of the problem.

We will analyse different documents from various sources, underline variables and identify their relationships over time.

 

Basic Brain Drain System Dynamics Diagram

'Efficiencyism’  can be described as a blind belief in the effectiveness of efficiency measures without taking into account circumstances and the wider context.   The graph on the left shows how the frequent use of the term 'efficiency' at the level of local interactons can lead to the emergence of  'efficiencyism' through upward causation, denoted by the arrows pointing upwards.  However, there is also downward causation from the global level depicted by the red arrows which can increase the blind application of efficiency measures at the local level. In other words, efficiency for the sake of efficiency becomes a dominant idea.  The tyrannical influence of 'eficiencyism' affects all of us to varying degrees and unfortunately can often have very negative side effects, such as an increase in unemployment, social injustice and even increase inequality.  Of  course, well thought out efficiency improvements can also bring great  benefits.   I recommend reading an excellent article by Dr. Charles Chandler, who explains the term 'efficiencyism' with some excellent examples and also points to some  of its undesirable effects.

http://www.ageofoe.com/010-efficiencyism-holds-us-back/

Description:

A simple model of economic growth where a government taxes the economy, and spends it on capital and revenue goods.

A detailed description of all model input parameters is available here. These are discussed further here and here.


Update 18 December 2016 (v2.7): Added feature to calculate a global EROI index for all energy sources plus intermittency buffering (currently batteries only, but this could be diversified). The index is calculated specifically in terms of energy services in the form of work and heat. That is, it takes the aggregated energy services made available by all sources as the energy output term, and the energy services required to provided the buffered output as the energy input term.

Update 29 June 2016 (v2.6): Added historical emplacement for wind and PV capacity. The maximum historical emplacement rates are then maintained from year 114/115 until the end of the model period. This acts as a base emplacement rate that is then augmented with the contribution made via the feedback control mechanism. Note that battery buffering commences only once the additional emplacement via the feedback controller kicks in. This means that there is a base capacity for both wind and PV for which no buffering is provided, slightly reducing the energy services required for wind and PV supplies, as well as associated costs. Contributions from biomass and nuclear have also been increased slightly, in line with the earlier intention that these should approximately double during the transition period. This leads to a modest reduction in the contributions required from wind and PV.

Added calculation of global mean conversion efficiency energy to services on primary energy basis. This involves making an adjustment to the gross energy outputs for all thermal electricity generation sources. The reason for this is that standard EROI analysis methodology involves inclusion of energy inputs on a primary energy equivalent basis. In order to convert correctly between energy inputs and energy service inputs, the reference conversion efficiency must therefore be defined on a primary energy basis. Previously, this conversion was made on the basis of the mean conversion efficiency from final energy to energy services.

Update 14 December 2015 (v2.5): correction to net output basis LCOE calculation, to include actual self power demand for wind, PV and batteries in place of "2015 reference" values.

Update 20 November 2015 (v2.4): levelised O&M costs now added for wind & PV, so that complete (less transmission-related investments) LCOE for wind and PV is calculated, for both gross and net output.

Update 18 November 2015 (v2.3: development of capital cost estimates for wind, PV and battery buffering, adding levelised capital cost per unit net output, for comparison with levelised capital cost per unit gross output. Levelised capital cost estimate has been substantially refined, bringing this into line with standard practice for capital recovery calculation. Discount rate is user adjustable.

Default maximum autonomy periods reduced to 48 hours for wind and 72 hours for PV.

Update 22 October 2015 (v2.2): added ramped introduction of wind and PV buffering capacity. Wind and PV buffering ramps from zero to the maximum autonomy period as wind and PV generated electricity increases as a proportion of overall electricity supply. The threshold proportion for maximum autonomy period is user adjustable. Ramping uses interpolation based on an elliptical curve between zero and the threshold proportion, to avoid discontinuities that produce poor response shape in key variables.

Update 23 September 2015 (v2.1): added capital investment calculation and associated LCOE contribution for wind generation plant, PV generation plant and storage batteries.

**This version (v2.0) includes refined energy conversion efficiency estimates, increasing the global mean efficiency, but also reducing the aggressiveness of the self-demand learning curves for all sources. The basis for the conversion efficiencies, including all assumptions relating to specific types of work & heat used by the economy, is provided in this Excel spreadsheet.

Conversion of self power demand to energy services demand for each source is carried out via a reference global mean conversion efficiency, set as a user input using the global mean conversion efficiency calculated in the model at the time of transition commencement (taken to be the time for which all EROI parameter values are defined. A learning curve is applied to this value to account for future improvement in self power demand to services conversion efficiency.**

The original "standard run" version of the model is available here.

This is a simulation of monetary flows for a business that uses Circular Money.

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.