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.

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

This is a model that simulates the competition between logging versus adventure tourism (mountain bike riding) in Derby Tasmania. The simulation is borrowed from the Easter island simulation

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? 

A model situmalte the relationship between moutain bikes and logging industry in Derby, Tasmania, It explains more when the number of visitors increases or decreses. 

QPM model

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 upper diagram shows the principal factors that have an influence on the budget deficit and indicates what needs to be done to correct it. But this is not the full story. The diagram below shows that  cutting public expenditure reduces aggregate demand and  increases unemployment. The reduction of aggregate demand  reduces  economic activity which has the effect of reducing  tax revenue.  In addition, the state has to pay out funds as there is a need for more unemployment benefit payments.   The result of these austerity measures  is often the opposite of their intended purpose: they can increase rather than decrease the budget deficit.

There is plenty of empiric evidence to show that this has happened time and time again. For instance, a report from UNCTAD (United Nations Conference on Trade and Development) found that between 1990 and 2000 in all the  cases examined where cutbacks in public spending and tax increases were used, the fiscal situation did not only not improve but worsened. Despite such repeated evidence, unfortunately calls for  austerity measures continue to be heard. 

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.

Overview 

A simple model simulates the conflict between adventure tourism (mountain biking) and logging in Derby, Tasmania. It demonstrates how these industries co-exist and in what circumstances would affect the interests of both parties. 

How does the model work? 

The demand for mountain biking came from visitors' enjoyment of nature and desire for scenery. Adventure is driven by the excitement of visitors with their experience and friends' recommendations.  

The demand for timber leads to the amount of logging, and its price per log impacts forest revenue. It brought employment opportunities to the local residents in Derby Mountain. The excessive deforestation affects landscapes and scenery, so regrowth is essential. 

Interesting Insights 

The major rebate is reducing park spaces will degrade visitors' experience of enjoyment with nature. Still, at the same time, logging brings significant business benefits to the local residents.  The environmental effect of being well-managed between mountain bikes and logging needs to be depth-explored and balanced. 

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%.

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

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.