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! 

Wealth can be seen as the factories, infrastructure, goods and services the population of a nation dispose of. According to Tim Garrett,  a scientist who looks at the economy from the perspective of physics, it is existing wealth that generates economic activity and growth. This growth demands the use of energy as no activity can take place without its use. He also points out that the use of this energy unavoidably  leads to concentrations of CO2 in the atmosphere.  All this, Tim Garrett says,  follows from the second law of thermodynamics.  If wealth decreases then so does economic activity and growth. The CLD tries to illustrate how wealth, ironically, now generates the conditions and feedback loops  that  may cause it to decline. The consequences are  inevitably economic  stagnation (or secular recession?). 

You can read about the connection Tim Garrett makes between 'Wealth, Economic Growth, Energy and CO2  Emissions' simply by Googling 'Tim Garrett and Economy'.

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.

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

To maintain economic wealth (roads, hospitals, power lines, etc.) power needs to be consumed. The same applies to economic activity, since any activity requires the consumption of energy. According to the Environmental Protection Agency, the burning of fossil fuels was responsible for 79 percent of U.S. greenhouse gas emissions in 2010. So whilst economic activity takes place fossil fuels will be burned and CO2 emissions are unavoidable - unless we use exclusively renewable energy resources, which is not likely to occur very soon. However, the increasing CO2 concentrations in the atmosphere will have negative consequences, such droughts, floods, crop failures, etc. These effects represent limits to economic growth. The CLD illustrates some of the more prominent negative feedback loops that act as a break on economic growth and wealth.  As the negative feedback loops (B1-B4) get stronger, an interesting question is, 'will a sharp reduction in economic wealth and unavoidable recession lead to wide-spread food riots and disturbances?'

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

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.

This is a Virtual Systemic Inquiry (VSI) Project. Please refer to the Stimulating an Economy focus page.

A model to gain understanding of the causes and effects of a population's interest in engineering.

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.

This model which simulates the competition of Logging with Mountain Tourism in Derby, Tasmania.  This main reason of this simulation is to find if logging will affect the mountain tourism and by any chance they can co-exist.

Neoliberalism uses a deceptive narrative to declare that money the government spends into the economy in excesses of the taxes it collects creates a ‘government debt’. In fact, the money the government spends into the economy in excess of the taxes is an income, a benefit for the private sector. When the government issues bonds, the money the private sector uses to buy them via banks comes from a residual cushion of dollars that the government already spent into the economy but has not yet taxed back.  If this were not the case, if the government had taxed back all the money it spent into the economy, then the economy could not function. There would be no dollars in the economy, since the government is the sole supplier of U.S. dollars! In the doted rectangle in the graph you can see that the dollars paid to the government for bonds sits in a dollar asset account. When the government issues bonds it simply provides the public and institutions with a desirable money substitute that pays interest i.e. Treasury bonds. It is a swap of one kind of financial asset for another. To register this swap the government debits the dollar asset account and credits the bond account.  When the time comes to redeem (take back) the bonds, all the government does is revers the swap, and that’s all!  When you look at the total amount of finacial assets in the private sector,  these remain constant at $ 25 BN  after the payment of $ 5 BN taxes. This implies that  no lending of financial assets of the private sector to the government has taken place during the swap operation. The money was always there. The debt mountain is an illusion!

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

Description:

Rich picture trying to explain in detail the economy of Peru.

First Basic Macro economic model

This is a system dynamic model to describe relationship between local logging industry and biking tourism in Tasmanian Derby Mountain.

In the dynamic model, the left-hand side shows how Derby get income from local biking tourism. The biking visitors number are influenced by scenery evaluation which depend on local size of forest and influenced government policy support when Biking Tourism income is over 1000 unit. Biking visitors with good recommendation will also back to Mountain Derby and bring income for local in twice or more times.  In the right-hand side, we found the income of logging industry was influenced by local logging growth rate and government policy if local Biking Tourism income is over 1000 unit. The increase of logging industry will also increase local employment which will influence employee cost. This factor will also affect total logging income in Derby Mountain.

 

The simulation results show, with governments support the Biking tourism will increase sharply in the first few years and finally instead local logging industry, at same time bring good environment and save local forest under local increase logging industry. The recommendation graph shows that, the number of good recommendation & bad recommendation for Derby Mountain biking tourism will also increase in high speed in front of few years with data fluctuation but finally maintain in a stable line. Last simulation graph shows that how policy factor influences logging and biking industry. The Government has strong support in local tourism, however, as number of tourists increase, the positive impact from government support will continue decrease. On the contrary, the government support influence will also decease to local logging industry when logging been instead by tourism. 

Model supporting research of investment vs. austerity implications. Please refer to Modern Money & Public Purpose Video.

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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).