Overview 

This model not only reveals the conflict between proposed logging of adjacent coups and Mountain bike in Derby but also simulates competition between them. The simulation model aims to investigate the potential coexistence opportunities between the mountain biking and forestry and find out the optimal point for coexistence to help improve Tasmania’s economy. 

 

How the model works 

It is recognized that the mountain biking and forestry industries can help support the Tasmanian community and strengthen the Tasmanian economy. The logging and forest sector in Derby can help the local community generate wealth and create more employment opportunities. The sector main source of income come from selling timber such as domestic and export sales. Nevertheless, the sector’s profit has decreased over the past few years on account of the weaker demand and reduced output. Accordingly, the profitability and output of the sector have fluctuated in response to the availability of timber, the timber price movements as well as the impact of changing demand conditions in downstream timber processing sectors. The slow growth rate for a timber has a negative impact on the profitability of the forestry industry and the economic contribution of this industry is set to grow slower, as there is a positive correlation between these variables. In addition, the mountain biking industry in Derby can bring a huge significant economic contribution to the local community. The revenue streams of the industry come from bike rental, accommodation, retail purchase and meals and beverages. These variables also influence the past experience which is positive correlation between reviews and satisfaction that can impact the demand for the mountain biking trails. More importantly, the low regeneration rate for a timber can have a negative impact on the landscape of the mountain biking and the tourist’s past experience that led to a decrease in the demand of tourists for the mountain biking, as the reviews and satisfaction are dependent on the landscape and past experience. It is evident that the industry not only helps the local community generate wealth through industry value addition but also creates a lot of employment opportunities. Therefore, the Mountain Bike Trails can be regarded as sustainable tourism that can help increase employment opportunities and economic contribution that can be of main economic significance to the Tasmania’s economy. Therefore, both industries can co-exist that can maximise the economic contribution to the local community and the Tasmanian economy.

Interesting Insights

It is interesting to note that the activity of cutting down trees does not influence the development of Mountain Biking industry. By lowering the prices of accommodation, food, bike rental and souvenirs, it can help increase the reviews and recommendations of Mountain Biking that will enhance the number of tourists. In this case, the Mountain Biking industry can achieve sustainable economic growth in the long-term while the economic growth rate of forestry industry will continue to decrease. 

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

Z414 from System Zoo 2

Could repeat expositions of the truth about money creation undermine the wide-spread erroneous belief that governments cannot spend more money than they collect in taxes unless they borrow? Governments, via their central banks, can create as much of their currency as they wish. They can never be forced into default on debt obligations issued in their own currency. In fact, they do not even need to issue debt, as the false dogma tries to make us believe - why should a government have to borrow its own currency when it can create it? Could the dynamic indicated by the CLD be used to spread the long-overdue acceptance of monetary reality? 

Description:

Update 24 Feburary 2016 (v3.1): This version has biomass, hydro and nuclear continuing at pre-transition maxima, rather than increasing. The combined emplacement rate cap for wind and PV is set at a default value of 5000 GW/year.

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.

Model description: 

This model is designed to simulate the Covid-19 outbreak in Burnie, Tasmania by estimating several factors such as exposed population, infection rate, testing rate, recovery rate, death rate and immunity loss. The model also simulates the measures implemented by the government which will impact on the local infection and economy. 

 

Assumption:

Government policies will reduce the mobility of the population as well as the infection. In addition, economic activities in the tourism and hospitality industry will suffer negative influences from the government measures. However, essential businesses like supermarkets will benefit from the health policies on the contrary.

 

Variables:

Infection rate, recovery rate, death rate, testing rate are the variables to the cases of Covid-19. On the other hand, the number of cases is also a variable to the government policies, which directly influences the number of exposed. 

 

The GDP is dependent on the variables of economic activities. Nonetheless, the government’s lockdown measure has also become the variable to the economic activities. 

 

Interesting insights:

Government policies are effective to curb infection by reducing the number of exposed when the case number is greater than 10. The economy becomes stagnant when the case spikes up but it climbs up again when the number of cases is under control. 

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

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

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 a compensation 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 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).

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

QPM model

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

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

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

Update 26 October 2017 (v2.7): Updated historical wind and PV deployment data for 2015-2016, adding projected PV deployment for 2017. Data via https://en.wikipedia.org/wiki/Growth_of_photovoltaics and https://en.wikipedia.org/wiki/Wind_power_by_country.

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.

ABOUT THE MODEL

This is a dynamic model that shows the correlation between the health-related policies implemented by the Government in response to COVID-19 outbreak in Burnie, Tasmania, and the policies’ impact on the Economic activity of the area.

 ASSUMPTIONS

The increase in the number of COVID-19 cases is directly proportional to the increase in the Government policies in the infected region. The Government policies negatively impact the economy of Burnie, Tasmania.

INTERESTING INSIGHTS

1. When the borders are closed by the government, the economy is severely affected by the decrease of revenue generated by the Civil aviation/Migration rate. As the number of COVID-19 cases increase, the number of people allowed to enter Australian borders will also decrease by the government. 

2. The Economic activity sharply increases and stays in uniformity. 

3. The death rate drastically decreased as we increased test rate by 90%.

Basic Brain Drain System Dynamics Diagram