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%.

C8 Article through the Economy lense

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

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

From Warren C. Sanderson in Population - Development - Environment, Wolfgang Lutz (Ed.), 1994, Springer.

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.

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

this is economy as it is in reality.

Z414 from System Zoo 2

Muhammad Firas Zahid Suryaatmaja

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. 

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 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.

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. 

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

Version work

Overview

A model which simulates the competition between logging versus adventure tourism (mountain bike ridding) in Derby Tasmania. 

How the model works:

Trees grow, and we cut them down because of the demand for Timber and sell the logs. Mountain bikers and holiday visitors will come to the park and this depends on experience and recommendations.  Past experience and recommendations depend on the Scenery, number of trees compared to the visitor and Adventure number of trees and users.  Park capacity limits the number of users.  To utilize highest park capacity, they need to promote to the holiday visitor segment as well. Again, the visit depends on the scenery. So, both mountain biking and forestry (logging) businesses need to contribute a significant amount of revenue to CSR for faster regrowth of trees.

Interesting insights

It looks like a lot of logging doesn't stop people from mountain biking. 

Faster replantation of the tree will balance out the impact created by logging which will give the visitor a positive experience and the number of visitors is both improved. 

To keep the park's popularity in check, the price of wood needs to be high. 

Also, it looks like mountain biking only needs a narrow path.

CSR contribution to nature can be a crucial factor.