Simple epidemiological model for Burnie, Tasmania   SIR: Susceptible to infection - Infected - Recovery, Government responses and Economic impacts           Government policy is activated when there are 10 or fewer reported cases of COVID-19. The more people tested, the fewer people became infected
Simple epidemiological model for Burnie, Tasmania
SIR: Susceptible to infection - Infected - Recovery, Government responses and Economic impacts  

Government policy is activated when there are 10 or fewer reported cases of COVID-19. The more people tested, the fewer people became infected. So the government's policy is to reduce infections by increasing the number of people tested and starting early. At the same time, it has slowed the economic growth (which, according to the model,  will stop for next 52 weeks).
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" value
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.
国連が公表している人口の将来推計とOECDが公表している各種経済統計を参考にして、2000年から2100年までの人口・経済見通しを作成するためのダイナミクスモデル。     ①人口:年少(0-14歳)・再生産年齢人口(15-49歳)・後期生産年齢人口(50-64歳)・老年人口(65歳以上)にグループ分けし、出生数(再生産年齢人口×出生率)と死亡数(年代別死亡率×年代別人口の合計)を算出して総人口を推計     ②経済:2000年のGDPをストックとして、コブ=ダグラス型関数に基づき労働力人口(15歳以上人口×労働参加率)と資本ストック(総固定資本形成)および全要素生産性の成長率をフローとし、購
国連が公表している人口の将来推計とOECDが公表している各種経済統計を参考にして、2000年から2100年までの人口・経済見通しを作成するためのダイナミクスモデル。

①人口:年少(0-14歳)・再生産年齢人口(15-49歳)・後期生産年齢人口(50-64歳)・老年人口(65歳以上)にグループ分けし、出生数(再生産年齢人口×出生率)と死亡数(年代別死亡率×年代別人口の合計)を算出して総人口を推計

②経済:2000年のGDPをストックとして、コブ=ダグラス型関数に基づき労働力人口(15歳以上人口×労働参加率)と資本ストック(総固定資本形成)および全要素生産性の成長率をフローとし、購買力平価レートの変化率も加味して将来のGDP(購買力平価換算)を算出

現状投影シナリオ:2000年から2100年までに制度や前提条件の極端な変更はなく、現状のトレンドが続くと想定される場合
24 11 months ago
Afirmația că nu poate exista activitate economică fără energie și că combustibilii fosili sunt finiți contrastează cu faptul că banii nu sunt finiți și pot fi creați de guverne prin intermediul băncilor lor centrale la costuri marginale zero ori de câte ori este nevoie.
         Un fapt important de
Afirmația că nu poate exista activitate economică fără energie și că combustibilii fosili sunt finiți contrastează cu faptul că banii nu sunt finiți și pot fi creați de guverne prin intermediul băncilor lor centrale la costuri marginale zero ori de câte ori este nevoie.
Un fapt important despre cărbunele, gazul și petrolul (mai ales atunci când sunt produse prin fracking) este că raporturile lor energetice nete scad rapid. Cu alte cuvinte, energia necesară pentru a extrage o anumită cantitate de combustibili fosili este în continuă creștere. Raportul în scădere „EROI” (Returul Energiei asupra Energiei Investite) oferă încă un avertisment că nu ne mai putem baza pe combustibilii fosili pentru a ne alimenta economiile. În 1940 a fost nevoie de energia unui singur baril de petrol pentru a extrage 100. Astăzi, energia unui baril de petrol va da doar 15. Nu putem aștepta până când raportul scade la 1/1 înainte de a investi serios în surse alternative de energie, pentru că până atunci societatea industrială așa cum o cunoaștem în prezent va fi încetat să mai existe. Un EROI de 1:1 înseamnă că este nevoie de energia unui baril de petrol pentru a extrage un baril de petrol - producția de petrol s-ar opri pur și simplu!

 ​In a recent report, the World Economic Forum
considered that the use of robots in economic activity will cause far more job
losses in the near future than there will be new ones created. Every economic
sector will be affected. The CLD tries to illustrate the dynamic effects of
replacing human work
​In a recent report, the World Economic Forum considered that the use of robots in economic activity will cause far more job losses in the near future than there will be new ones created. Every economic sector will be affected. The CLD tries to illustrate the dynamic effects of replacing human workers with robots. This  dynamic  indicates that if there is no replacement of the  income forgone by the laid off workers, then the economy will soon grind to a halt. To avoid disaster, there must be enough money in circulation, not parked in off-shore investments, to permit the purchase of all the goods and services produced by robots. The challenge for the government is to make sure that this is  case.  

   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 fi

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       Assumptions   Govt policy reduces infection and
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

Assumptions
Govt policy reduces infection and economic growth in the same way.

Govt policy is trigger when reported COVID-19 case are 10 or less.

A greater number of COVID-19 cases has a negative effect on the economy.  This is due to economic signalling that all is not well.

Interesting insights

Higher testing rates seem to trigger more rapid government intervention, which reduces infectious cases.  The impact on the economy though of higher detected cases though is negative. 




Rich picture trying to explain in detail the economy of Peru. Year: 2017
Rich picture trying to explain in detail the economy of Peru.
Year: 2017
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 o
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.
   Explanation of the Model    This is a Model of COVID-19 outbreak in Burnie, Tasmania which shows the government actions in response to the pandemic COVID-19 and its affects on the Economy. The government health policy changes depending on the reported cases, which is a dependent upon the testing
Explanation of the Model
This is a Model of COVID-19 outbreak in Burnie, Tasmania which shows the government actions in response to the pandemic COVID-19 and its affects on the Economy. The government health policy changes depending on the reported cases, which is a dependent upon the testing rate. 

Assumptions
Lockdown and travel ban were the main factor in government policy. It negatively impacts on the Economic growth as individuals are not going out which is directly affects the business around the world, in this insight 'Burnie'. This reduces the economic growth and the factors positively effecting economic growth such as Tourism.

Government policies has a negative impact on Exposer of individuals. Moreover, it also has a negative impact on chances of infection when exposed as well as other general infection rate.
 

Interesting Insight 
There is a significant impact of test rating on COVID-19 outbreak. Higher rates increases the government involvement, which decreases cases as well as the total death. 
In contrast, lower testing rates increase the death rate and cases. 

Tourism which plays a avital role in Tasmanian Economy greatly affects the Economic Growth. The decline of Tourism in parts of Tasmania such as Burnie, would directly decrease the economy of Tasmania.


  
  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 mountai

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. 

This model simulates the competition between logging versus adventure tourism(mountain bike riding) in Derby Tasmania. The purpose of this model is that focus on the relationship between the timber industry and mountain bike tourism in adventure. It also reflects how well these two industries co-exi
This model simulates the competition between logging versus adventure tourism(mountain bike riding) in Derby Tasmania. The purpose of this model is that focus on the relationship between the timber industry and mountain bike tourism in adventure. It also reflects how well these two industries co-exist. 

How this model works
This model shows tree grow development. In order to maximize the profits from selling the logging, the demand for timbers will increase. 
The mountain bike visits depend on past experience and recommendations. In addition, past experience and recommendations depend on Scenery, which is determined by the number of trees and visitors and adventure number. However, park capacity limits the number of use mountain bikes, because the convince of parking is a consideration for the visitors. 
It seems like the high logging sale does not deter mountain bike activities. By reducing the parking capacity, visitor experience and number are increased. Because of the strong relationship between the mountain bike park and the explosion in visitor numbers. With the improvement in the number of visitors, the number of food and restaurants will go up as well. Because of the daily needs of the visitors. 

  Overview     This model simulates logging and mountain biking competition in Derby, Tasmania. The Simulation is referenced to simulate Derby mountain biking with logging.      Model   W  ork     The tourism industry is represented on the model's left side, and the logging industry is on the right

Overview

This model simulates logging and mountain biking competition in Derby, Tasmania. The Simulation is referenced to simulate Derby mountain biking with logging.

 

Model Work

The tourism industry is represented on the model's left side, and the logging industry is on the right side. Interactions between these two industries generate tax revenues. Logging and tourism have different growth rates regarding people working/consuming. The initial values of these two industries in the model are not fixed but increase yearly due to inflation or economic growth.

 

Detail Insights

From the perspective of tourism, as the number of tourists keeps growing, the number of people who choose to ride in Derby City also gradually increases. And the people who ride rate the ride. The negative feedback feeds back into the cycling population. Similarly, positive cycling reviews lead to more customer visits. And all the customers will create a revenue through tourism, and a certain proportion of the income will become tourism tax.

From a logging perspective, it is very similar to the tourism industry. As the number of people working in the industry is forecast to increase, the industry's overall size is predicted to grow. And as the industry's size continues to rise, the taxes on the logging industry will also continue to rise. Since logging is an industry, the tax contribution will be more significant than the tourism excise tax.

 

This model assumption is illustrated below:

1. The amount of tax reflects the level of industrial development.

2. The goal of reducing carbon emissions lets us always pay attention to the environmental damage caused by the logging industry.

3. The government's regulatory goal is to increase overall income while ensuring the environment.

4. Logging will lead to environmental damage, which will decrease the number of tourists.

 

This model is based on tourism tax revenue versus logging tax revenue. Tourism tax revenue is more incredible than logging tax revenue, indicating a better environment. As a result of government policy, the logging industry will be heavily developed in the short term. Growth in the logging industry will increase by 40%. A growth rate of 0.8 and 0.6 of the original is obtained when logging taxes are 2 and 4 times higher than tourism taxes.

 

Furthermore, tourism tax and logging tax also act on the positive rate, which is the probability that customers give a positive evaluation. The over-development of the logging industry will lead to the destruction of environmental resources and further affect the tourism industry. The logging tax will also affect the tourism Ride Rate, which is the probability that all tourism customers will choose Derby city.

 

This model more accurately reflects logging and tourism's natural growth and ties the two industries together environmentally. Two ways of development are evident in the two industries. Compared to tourism, logging shows an upward spiral influenced by government policies. Government attitudes also affect tourism revenue, but more by the logging industry. 

Overview This model is a working simulation of the competition between the mountain biking tourism industry versus the forestry logging within Derby Tasmania.    How the model works  The left side of the model highlights the mountain bike flow beginning with demand for the forest that leads to incre
Overview
This model is a working simulation of the competition between the mountain biking tourism industry versus the forestry logging within Derby Tasmania.

How the model works
The left side of the model highlights the mountain bike flow beginning with demand for the forest that leads to increased visitors using the forest of mountain biking. Accompanying variables effect the tourism income that flows from use of the bike trails.
On the right side, the forest flow begins with tree growth then a demand for timber leading to the logging production. The sales from the logging then lead to the forestry income.
The model works by identifying how the different variables interact with both mountain biking and logging. As illustrated there are variables that have a shared effect such as scenery and adventure and entertainment.

Variables
The variables are essential in understanding what drives the flow within the model. For example mountain biking demand is dependent on positive word mouth which in turn is dependent on scenery. This is an important factor as logging has a negative impact on how the scenery changes as logging deteriorates the landscape and therefore effects positive word of mouth.
By establishing variables and their relationships with each other, the model highlights exactly how mountain biking and forestry logging effect each other and the income it supports.

Interesting Insights
The model suggests that though there is some impact from logging, tourism still prospers in spite of negative impacts to the scenery with tourism increasing substantially over forestry income. There is also a point at which the visitor population increases exponentially at which most other variables including adventure and entertainment also increase in result. The model suggests that it may be possible for logging and mountain biking to happen simultaneously without negatively impacting on the tourism income.
 Simple epidemiological model for Burnie, Tasmania   SIR: Susceptible to infection - Infected - Recovery, Government responses and Economic impacts           Government policy is activated when there are 10 or fewer reported cases of COVID-19. The more people tested, the fewer people became infected
Simple epidemiological model for Burnie, Tasmania
SIR: Susceptible to infection - Infected - Recovery, Government responses and Economic impacts  

Government policy is activated when there are 10 or fewer reported cases of COVID-19. The more people tested, the fewer people became infected. So the government's policy is to reduce infections by increasing the number of people tested and starting early. At the same time, it has slowed the economic growth (which, according to the model,  will stop for next 52 weeks).
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/Growt
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.
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" value
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 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       Assumptions   Govt policy reduces infection and
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

Assumptions
Govt policy reduces infection and economic growth in the same way.

Govt policy is trigger when reported COVID-19 case are 10 or less.

A greater number of COVID-19 cases has a negative effect on the economy.  This is due to economic signalling that all is not well.

Interesting insights

Higher testing rates seem to trigger more rapid government intervention, which reduces infectious cases.  The impact on the economy though of higher detected cases though is negative. 




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       Assumptions   Govt policy reduces infection and
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

Assumptions
Govt policy reduces infection and economic growth in the same way.

Govt policy is trigger when reported COVID-19 case are 10 or less.

A greater number of COVID-19 cases has a negative effect on the economy.  This is due to economic signalling that all is not well.

Interesting insights

Higher testing rates seem to trigger more rapid government intervention, which reduces infectious cases.  The impact on the economy though of higher detected cases though is negative. 




 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 t

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. 

The statement that there can be no economic activity
without  energy and that fossil fuels are
finite contrasts with the fact that money is not finite and can be created by governments
via their central banks at zero marginal cost whenever needed.

 An important fact about COAL, GAS and OIL (especia
The statement that there can be no economic activity without  energy and that fossil fuels are finite contrasts with the fact that money is not finite and can be created by governments via their central banks at zero marginal cost whenever needed.

An important fact about COAL, GAS and OIL (especially when produced via fracking) is that their net energy ratios are falling rapidly. In other words the energy needed to extract a given quantity of fossil fuels is constantly increasing. The falling ratio 'EROI' (Energy Return on Energy Invested ) provides yet another warning that we can no longer rely on fossil fuels to power our economies. In 1940 it took the energy of only one barrel of oil to extract 100. Today the energy of 1 barrel of oil will yield only 15. We cannot wait until the ratio falls to 1/1 before we invest seriously in alternative sources of energy, because by then industrial society as we know it doday will have ceased to exist. An EROI of 1:1 means that it takes the energy of one barrel of oil to extract one barrel of oil - oil production would simply stop!