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

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 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 a reconstruction of the SIMM model presented in Chapter 2 of Feedback Economics (Contemporary Systems Thinking)

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 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. 

Simple SD version of Wheaton 1999  stock flow representation of DiPasquale-Wheaton 4 Quadrant steady state model (4QM) from Eskanasi 2014 and Zhang 2018 theses

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

Z414 from System Zoo 2

Visão geral

O modelo mostra a conexão e o conflito da indústria entre o turismo florestal e o turismo de montanha em Derby, Tasmânia. O objetivo desta simulação é descobrir o ponto de equilíbrio para a coexistência.

Como funciona o modelo?

Ambas as indústrias podem fornecer contribuições económicas para a Tasmânia. Em primeiro lugar, a venda de madeira através da exploração madeireira geraria renda. Além disso, os gastos dos ciclistas de montanha gerariam renda. No entanto, a baixa taxa de regeneração das árvores não pode encobrir a exploração madeireira, o que influencia as belas vistas e as experiências dos ciclistas. Embora a satisfação e a expectativa dependam das opiniões e da experiência, a demanda pelo mountain bike também seria influenciada pelas visitas repetidas e pelo boca a boca.

Informações interessantes

Embora a silvicultura possa fornecer uma grande contribuição económica para a Tasmânia, o excesso de exploração madeireira vai contra a estrutura ESG, além de criar conflito com o turismo de montanha. Desde que o número de visitas de cavaleiros seja estável, o turismo pode sempre proporcionar uma maior contribuição económica em comparação com a silvicultura. Portanto, o governo deveria considerar o ponto de equilíbrio entre as duas indústrias.

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.

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

This model is designed to simulate the outbreak of Covid-19 in Burnie in Tasmania, death cases, the governmental responses and Burnie local economy. 

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. 

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 

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. 

Version work

Muhammad Firas Zahid Suryaatmaja