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

Afirmația că nu poate exista activitate economică fără energie și că combustibilii fosili sunt finiți contrastează cu faptul că banii nu sunt finiți și pot fi creați de guverne prin intermediul băncilor lor centrale la costuri marginale zero ori de câte ori este nevoie.

A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy.  Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover

Overview
This model is a working simulation of the competition between the mountain biking tourism industry versus the forestry logging within Derby Tasmania.

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).

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).

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

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.

 

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

A sample model for class discussion modeling COVID-19 outbreaks and responses from government with the effect on the local economy.  Govt policy is dependent on reported COVID-19 cases, which in turn depend on testing rates less those who recover

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 (especially 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. The falling ratio 'EROI' (Energy Return on Energy Invested ) provides yet another warning that we can no longer rely on fossil fuels to power our economies. In 1940 it took the energy of only one barrel of oil to extract 100. Today the energy of 1 barrel of oil will yield only 15. 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. An EROI of 1:1 means that it takes the energy of one barrel of oil to extract one barrel of oil - oil production would simply stop! 

When people talk about a government deficit, they forget that this is only one side of the ledger. On the other is a corresponding non-government SURPLUS. The money the government spends is not lost but shows up in the private sector as income. When one talks only of the deficit then one can understand that many think it should be reduced or even converted into a surplus, but reducing the government deficit reduces private sector income and a government surplus forces a deficit on the private sector with a potentially devastating effect on private sector wealth and economic activity.  Unless the economy is overheating, government deficits are usually healthy. For countries that run traditionally a trade deficit, such as the US they are necessary to maintain economic activity. Consider this fact: for almost all of past 40 years the US and the UK have run deficits without any harmful effects!

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

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.

The Cred System is an alternative to traditional currency that increases community resiliency and reduces participant's dependence on traditional dollars. This model is a basic description of the Cred System, involving four people and two loops.

Major update 12 December 2015 (v3.0): This new version of the model overhauls the way that incumbent energy source (fossil sources plus biomass, hydro electricity and nuclear electricity) supply capacity is implemented. This is now based on direct (exogenous) input of historical data, with the future supply curve also set directly (but using a separate input array to the historical data). For coal and natural gas fired electricity, this also requires that the simple, direct-input EROI method be used (i.e. same as for coal and NG heating, and petroleum transport fuels).

Note that this new version of the model no longer provides a historical view of the emplacement rates for energy supply sources other than wind and PV, and therefore no longer allows comparison of required emplacement rates for wind and PV with incumbent energy sources. Output data relating to this is available in model version v2.5 (see link below), for the specific transition duration built into that version of the model.

The previous version of the model (version 2.5) is available here.

The original "standard run" version of the model (v1.0) is available here.

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 ''Ooop! 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