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A toy model to see what happens to employment when people must move through various states to get to certain jobs
Clone of Basic Employment Model
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Clone of Recycling and Waste Treatment in Vancouver
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This model shows the changing happened in forest industry and mountain tourism in Derby Tasmania. Logging will degrade mountain tourism while benefit the forestry industry. Simulation borrowed from the Easter Island simulation.

According to the analysis, logging does not reduce tourism income. With the increase of number of bike guide, tourism income will increase as well. Also, in forest industry, timber income is higher than the harvest spending which means the industry always gain profits from logging. Therefore, the main concern is that the logging should be balanced between the Mountain Tourism and the forest industry.
Simulation of Derby Mountain bikes versus logging
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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.

Simulação de Mountain Bikes Derby versus Silvicultura
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Clone of Clone of Factors affecting Brazilian soy export growth
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Clone of Elements of Human Security
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This is a reconstruction of the SIMM model presented in Chapter 2 of Feedback Economics (Contemporary Systems Thinking)

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Clone of Simple Macroeconomic Model (SIMM) (SFD)
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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.
Clone of Energy transition to lower EROI sources (v2.5)
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This is a reconstruction of the SIMM model presented in Chapter 2 of Feedback Economics (Contemporary Systems Thinking)

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Simple Macroeconomic Model (SIMM) (SFD)
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Clone of Factors affecting Brazilian soy export growth
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This is a reconstruction of the SIMM model presented in Chapter 2 of Feedback Economics (Contemporary Systems Thinking)

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Clone of Simple Macroeconomic Model (SIMM) (SFD)
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British monetary policy
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Factors affecting Brazilian soy export growth
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this is economy as it is in reality.
economy
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Overview
A model which simulates the competition between logging versus adventure tourism (mountain bike ridding) in Derby Tasmania.  Simulation borrowed from the Easter Island simulation.

How the model works.
Trees grow, we cut them down because of demand for Timber amd sell the logs.
With mountain bkie visits.  This depends on past experience and recommendations.  Past experience and recommendations depends on Scenery number of trees compared to visitor and Adventure number of trees and users.  Park capacity limits the number of users.  
Interesting insights
It seems that high logging does not deter mountain biking.  By reducing park capacity, visitor experience and numbers are improved.  A major problem is that any success with the mountain bike park leads to an explosion in visitor numbers.  Also a high price of timber is needed to balance popularity of the park. It seems also that only a narrow corridor is needed for mountain biking
Clone of Simulation of Derby Mountain biking versus logging
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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
Housing dynamics 1
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ISCI 360 - Project Finale
Clone of Sustainability in Fisheries Finale
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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.
Clone of Immigrant Populations and Policy Implications
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Z414 from System Zoo 2
Bossel: Z414 Resource Discovery
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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. 

Simulation Of Derby Mountain Bikes Versus logging
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This is a model that will simulate a medieval fantasy population with regular trades
Fantasy Simulation
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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.
Immigrant Populations and Policy Implications
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Clone of Clone of Clone of Clone of Recycling and Waste Treatment in Vancouver
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This is a reconstruction of the SIMM model presented in Chapter 2 of Feedback Economics (Contemporary Systems Thinking)

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Clone of Clone of Simple Macroeconomic Model (SIMM) (SFD)