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Economic forecast model

Peak oil will occur when it is too expensive to bring oil to the surface and not when reserves reach their limit. Companies must make a profit to be able to extract oil and stay in the oil business. However, that endeavour is becoming more and more difficult because of diminishing returns. They have to dig ever deeper to get to the oil at ever increasing costs, and the oil they find deep down is of a lesser quality. We have now reached a point where the price needed by oil companies to make a profit and stay in business is far higher than the price the market can bear. That price is probably about $ 100 per barrel - and rising every year! A market price o $ 100 will almost certainly cause a sharp recession and cause the price of oil to fall back beyond the point of profitability. For example, the combined profit of ExxonMobile, Chevron and Conocophillips fell from 80.4 billion in 2011 to only 3.7 billon in 2016 - see URL below. What the market can bear depends on the spending power of the mass of non-elite workers. The CLD shows the negative feedback loops that prevent oil prices to rise above the level of affordability. If non-elite workers cannot afford the goods and services offered, then there will be no demand for them and by extension for oil. In this situation the market price will not the cover the cost that oil companies need to extract oil. Oil supplies will decline and so will economic activity!

https://srsroccoreport.com/the-blood-bath-continues-in-the-u-s-major-oil-industry/

THE PRICE TRAP AND PEAK OIL

Hanns-Jürgen Hodann

Socio-economic

Taylor Gariepy

This model is designed for the local government of Burnie, Tasmania, aiming to help with balancing COIVD-19 and economic impacts during a possible outbreak.

The model has been developed based upon the SIR model (Susceptible, Infected, Recovered) model used in epidemiology.

It lists several possible actions that can be taken by the government during a COVID-19 outbreak and provide the economic impact simulation.

The model allow users to Change the government policies factors (Strength of Policies) and simulate the total economic impact.

Interestingly, the government plicies largely help with controlling the COVID outbreak. However, the stronger the policies are, the larger impact on local economy

Burnie Covid Model, Zilin Huang 533476

Zilin Huang

Case 1

April Rose Alpha

Model Explanation:

This system dynamics model visualises the impact on investment into policing and community engagement resources on the crime rates within the youth population of Bourke, NSW.
The model also adds in the variable of funding for safe houses. With a high rate of domestic violence, unfavorable home conditions and other socio-economic factors, many youth roam the streets with no safe place to go, which may lead to negative behaviour patterns.

Assumptions

Youth Population: 700
Total youth population in 2016 for Bourke LGA was 646 (ages 10-29). (Census, 2016) Figures rounded to 700 for purposes of this model simulation.

Constants:
70% registration and engagement rates for Community funded programs
30% attendance rate for Safe Houses
50% crime conviction rate

Variables

Positive and Negative Influences

The model shows a number of key variables that lead youth to become more vunerable to commit a crime (such as alienation, coming from households with domestic violence, boredom and socio-economic disadvantages such as low income), as well as the variables that enhance the youth's likelihood to be a contributing member of the community (developing trusted relationships and connections with others, and having a sense of self worth, purpose and pride in the community). These factors (positive and negative) are aggregated to a single rate of 50% each for the purposes of the simulation, however each individual situation would be unique.

Police Funding / Resources

Police funding and resources means the number of active police officers attending to criminal activities, as well as prevention tactics and education programs to reduce negative behaviour. The slider can be moved to increase or decrease policing levels to view the impact on conviction rates. Current policing levels are approx 40 police to a population of under 3000 in Bourke.

Crime Rate

Youth crime rates in Australia were 3.33% (2016). Acknowledging Bourke crime rates are much higher than average, a crime rate of 40% is set initially for this model, but can be varied using the sliders.

Community Program Funding / Resources

Community Program Funding and Resources means money, facilities and people to develop and support the running of programs such as enhancing employability through mentorship and training, recreational sports and clubs, and volunteering opportunities to give back to the community. As engagement levels in the community programs increase, the levels of crime decrease. The slider can be moved to increase or decrease funding levels to view the impact on youth registrations into the community programs.

Observations

Ideally the simulations should show that an increase in police funding reduces crime rates over time, allowing for more youth committing crimes to be convicted and subsequently rehabilitated, therefore decreasing the overall levels of youth at risk.

A portion of those youth still at risk will move to the youth not at risk category through increased funding of safe houses (allowing a space for them to get out of the negative behaviour loop and away), whom them may consider registering into the community engagement programs. An increase in funding in community engagement programs will see more youth become more constructive members of the community, and that may in turn encourage youth at risk to seek out these programs as well by way of social and sub-cultural influences.

Justice & Community Support Investment and the Impacts on Bourke Youth Population

Sarah Vassallo

5

Ocean/atmosphere/biosphere model tuned for interactive economics-based simulations from Y2k on.

Lab 13 Base Model

Jessica LeDuc

6

Decarbonization Stories

Dean M

12

Description

Model of Covid-19 outbreak in Burnie, Tasmania

This model was designed from the SIR model(susceptible, infected, recovered) to determine the effect of the covid-19 outbreak on economic outcomes via government policy.

Assumptions

The government policy is triggered when the number of infected is more than ten.

The government policies will take a negative effect on Covid-19 outbreaks and the financial system.

Parameters

We set some fixed and adjusted variables.

Covid-19 outbreak's parameter

Fixed parameters: Infection rate, Background disease, recovery rate.

Adjusted parameter: Immunity loss rate can be changed from vaccination rate.

Government policy's parameters

Adjusted parameters: Testing rate(from 0.15 to 0.95), vaccination rate(from 0.3 to 1), travel ban(from 0 to 0.9), social distancing(from 0.1 to 0.8), Quarantine(from 0.1 to 0.9)

Economic's parameters

Fixed parameter: Tourism

Adjusted parameter: Economic growth rate(from 0.3 to 0.5)

Interesting insight

An increased vaccination rate and testing rate will decrease the number of infected cases and have a little more negative effect on the economic system. However, the financial system still needs a long time to recover in both cases.

Untitled Insight

NGUYEN DANG KHOA VO

Final

Akshat Bansal

Unfortunately, this model only produces the illusion of functioning, but I did manage to get it to give me the graph. However, because of the use of flows, if you change the time step to and the simulation length to anything other than the same numbers, you'll find the graph showing something that looks more exponential. This is due to the function referencing itself in regards to time, so inevitably each time consumption grows it changes the outcome on the other side of the equation. Still, this is a convincing mock up. I added a "45 degree" line so that one could conceivably see (and also change) the difference made by altering the level of autonomous consumption.

Keynesian Macroeconomics

Nicholas Lowe

4

Overview:

Overall, this analysis showed a COVID-19 outbreak in Burnie, the government policies to curtail that, and some of the impacts it is having on the Burnie economy.

Variables

The simulation made use of the variables such as; Covid-19: (1): Infection rate. (2): Recovery rate. (3): Death rate. (4): Immunity loss rate etc.

Assumptions:

From the model, it is apparent that government health policies directly affect the economic output of Burnie. A better health policy has proven to have a better economic condition for Burnie and verse versa.

In the COVID-19 model, some variables are set at fixed rates, including the immunity loss rate, recovery rate, death rate, infection rate, and case impact rate, as this is normally influenced by the individual health conditions and social activities.

Moving forward, we decided to set the recovery rate to 0.7, which is a rate above the immunity loss rate of 0.5, so, the number of susceptible could be diminished over time.

Step 1: Try to set all value variables at their lowest point and then stimulate.

Outcome: the number of those Infected are– 135; Recovered – 218; Cases – 597; Death – 18,175; GDP – 10,879.

Step 2: Try to increase the variables of Health Policy, Quarantine, and Travel Restriction to 0.03, others keep the same as step 1, and simulate

Outcome: The number of those Infected – 166 (up); Recovered – 249 (up); Cases – 554 (down); Death – 18,077 (down); GDP – 824 (down).

With this analysis, it is obvious that the increase of health policy, quarantine, and travel restriction will assist in increase recovery rate, a decrease in confirmed cases, a reduction in death cases or fatality rate, but a decrease in Burnie GDP.

Step 3: Enlarge the Testing Rate to 0.4, variable, others, maintain the same as step 2, and simulate

Outcome: It can be seen that the number of Infected is down to – 152; those recovered down to – 243; overall cases up to – 1022; those that died down to–17,625; while the GDP remains – 824.

In this step, it is apparent that the increase of testing rate will assist to increase the confirmed cases.

Step 4: Try to change the GDP Growth Rate to 0.14, then Tourism Growth Rate to 0.02, others keep the same as step 3, and then simulate the model

Outcome: what happens is that the Infected number – 152 remains the same; Recovered rate– 243 the same; Number of Cases – 1022 (same); Death – 17,625 (same); but the GDP goes up to– 6,632.

This final step made it obvious that the increase of GDP growth rate and tourism growth rate will help to improve the overall GDP performance of Burnie's economy.

The Recent COVID-19 Outbreak in Burnie Tasmania - Buchi Okafor 546792

Buchi Okafor

Clone of Pesticide Use in Central America for Lab work

This model is an attempt to simulate what is commonly referred to as the “pesticide treadmill” in agriculture and how it played out in the cotton industry in Central America after the Second World War until around the 1990s.

The cotton industry expanded dramatically in Central America after WW2, increasing from 20,000 hectares to 463,000 in the late 1970s. This expansion was accompanied by a huge increase in industrial pesticide application which would eventually become the downfall of the industry.

The primary pest for cotton production, bol weevil, became increasingly resistant to chemical pesticides as they were applied each year. The application of pesticides also caused new pests to appear, such as leafworms, cotton aphids and whitefly, which in turn further fuelled increased application of pesticides.

The treadmill resulted in massive increases in pesticide applications: in the early years they were only applied a few times per season, but this application rose to up to 40 applications per season by the 1970s; accounting for over 50% of the costs of production in some regions.

The skyrocketing costs associated with increasing pesticide use were one of the key factors that led to the dramatic decline of the cotton industry in Central America: decreasing from its peak in the 1970s to less than 100,000 hectares in the 1990s. “In its wake, economic ruin and environmental devastation were left” as once thriving towns became ghost towns, and once fertile soils were wasted, eroded and abandoned (Lappe, 1998).

Sources: Douglas L. Murray (1994), Cultivating Crisis: The Human Cost of Pesticides in Latin America, pp35-41; Francis Moore Lappe et al (1998), World Hunger: 12 Myths, 2nd Edition, pp54-55.

Clone of REM 221 - Causal Loop diagramming

Ben Boulanger

WIP Based on Steve Keen's Inaugural Kingston Lecture Youtube video slides models and data all at his blog

Is Capitalism Doomed to Crises

Geoff McDonnell

Model-SIM from Chapter 3 of Wynn Godley and Marc Lavoie's Monetary Economics, adapted for an open economy. The model is stock-flow consistent with only government money--no bills or bonds. No central bank and interest rates do not change. Government spends buying output from the production sector. The production sector is passive turning over all revenue over to households. Households save out of income and spend partially spend out of wealth. Imports and exports pass through the production sector illustrating the idea that consumer households buy from domestic businesses that which they have imported. The model also tracks the sectoral balance flows and changes in equity. Sectoral flows and equity balances match each other dollar for dollar to satisfy the sectoral balances accounting identity (Household Saving - Consumption) + (Business Saving - Expenditure) + (Taxes - Government Spending) - (Exports - Imports) = 0. Since business investment occurs internally to the Business Sector,

SFC_Model-SIM_open_economy

Aaron Medlin

4 months ago

Based on G.P. Cimellaro et al. Framework for analytical quantification of disaster resilience Engineering Structures 32 (2010) 3639–3649 paper

Facilities Disaster Resilience

Geoff McDonnell

An initial study of the economics of single use coffee pods.

Helene D. Coffee Pod Investigation

Helene D.

Socio-economic factors (kaya)

Olivia Barber

Overview of Part G Ch 27 to 30 of Mitchell Wray and Watts Textbook see IM-164967 for book overview

History of Macroeconomic Thought

Geoff McDonnell

6

3. PROBLEMAS e PERGUNTAS SOBRE projetos de SISTEMA INTEGRADO DE GESTÃO CUSTOS , INVESTIMENTOS BASEADO MODELOS MATEMÁTICOS: veja https://docs.google.com/document/d/1oGmItBcErhVF0PWuI37AnpP3n8Up3Zu45W7QB1MMbcY/edit?usp=sharing

Projeto de investimentos , custos e viabilidade econômico de LCC

A planta foi dimensionada para produzir 9.000 Ton/ano da Resina usando o matéria prima

LCC , operando 24h/dia, durante os três turnos por 300 dias/anuais. O preço do produto de projeto de lcc

veja o prova html aula passados

1. Calcule o investimento em planta (If) usando o método rápido e investimento em

equipamento (Ie) baseado no método de lang. Admita valor de N e f1 de acordo com o fluxograma do processo.

Dados fornecidos: Entrada (alimentação)-sólido; Saída-líquido;

Equipamentos principais da produção: Destilador e fermentador.

2. Calcule o investimento fixo total pelo método chilton através das estimativas dos investimentos fixos diretos: Tubulação, instrumentação, estrutura física, planta de serviço e conexões entre unidades; e investimentos fixos indiretos. Tome como base o investimento em equipamentos.

veja dados na prova html simulados sobre fator chiltons , modelos de lang , decico , chiltons e dados na prova html

3. Calcule o custo de mão-de- obra direta e indireta baseando-se no fluxograma de processo , atualizando o valor salário mínimo e nos salários:

Valor do salário mínimo = R$180,00

Engenheiro químico = 10 salários mínimos

Operador industrial = 3 salários mínimos

Administração:

Gerente = 8 salários mínimos

Auxiliar de escritório = 3 salários mínimos

Secretária = 2 salários mínimos

Dados fornecidos: Considere os encargos sociais de 65% sobre o salário base. Mão-de- obra

indireta seja 20% da mão-de- obra direta. O custo de mão-de- obra indireta engloba

manutenção.

4. Calcule os custos fixos abaixo, baseando-se pelo método Sebrae:

Dados

4.1 Depreciação = 10%If

4.2 Manutenção = 3%If

4.3 Seguro = 1%If

4.4 Imposto = 2%If

5. Calcule o custo de consumo anual de matéria-prima de acordo com os dados , veja prova html a seguir

5.2 Calcule o custo unitário de matéria prima sendo 80% do valor do custo total anual da

matéria-prima. , dados , veja na link enunciados e prova html

6. Calcule os custos totais:

6.1 Encargos anuais

6.2 Administração = 0.6 (mão-de- obra direta + mão-de- obra indireta + encargos anuais)

6.3 Suprimentos = 0.15 (Manutenção)

6.4 Calcule os custos fixos

6.5 Calcule os custos variáveis

6.6 Calcule os custos variáveis

* Os custos fixos englobam administração

Custo variável = custo de matéria – prima + custo de utilitários + custo de suprimentos.

Custo de suprimentos é 10% da mão-de- obra direta.

Depreciação = 10% do investimento fixo.

7. Estimar o ponto de equilíbrio em quantidade e em porcentagem baseado em dados obtidos de custo variável unitário) e Custo fixo do problema 06.

8. Estime os itens da análise de investimento:

– Taxa de retorno de engenharia simples

– Tempo de retorno

– % de lucro em relação ao preço de venda

– Lucro após o imposto de renda

– Lucratividade

– Rentabilidade

– Fluxo de caixa

9. Estimar potencial econômico de projeto de perdas devido ao baixo rendimento de operação em nível de 90% de rendimento máximo em vez de 98%.

Dados de consumos de materiais e energia obtidos via uso de calculadora usando quiz html de modelos já apresentados aula passos

NOME E DESCRIÇÃO

LINK

TAMANHO

Prova1validacao.:Investimento Fixo e Tomada de Decisões Rápidas

https://canvas.instructure.com/courses/780776/files/folder/provahtml?preview=51184101

33 KB

Prova.2 Validacao .Investimento fixo método Lang

https://canvas.instructure.com/courses/780776/files/folder/provahtml?preview=51184144

32 KB

Prova 3 :Investimento Fixo método Chilton

ihttps://canvas.instructure.com/courses/780776/files/folder/provahtml?preview=51184169

33 KB

Prova4:Custo Fixo

https://canvas.instructure.com/courses/780776/files/folder/provahtml?preview=51184189

32 KB

Prova 5::Custo de mao de obra

customaohtm custo de mao de obra

33 KB

Prova 6 Validao : Custo de mat,comb e enegia

CustoMat prima , energia

34 KB

Prova 7 Custo total

custo de operacional de producao

33 KB

Prova 8 Ponto deEquilibrio

ponto de eqilibrioibrio

32 KB

Prova 9:Analise de lucro e beneficios

Fluxo de caixa

33 KB

gestao economical planta resina fenolicas lcc .Modelos e resultados validados validado via planilhas

Pagandai V Pannir Selvam

Clone of Pesticide Use in Central America for Lab work

This model is an attempt to simulate what is commonly referred to as the “pesticide treadmill” in agriculture and how it played out in the cotton industry in Central America after the Second World War until around the 1990s.

The cotton industry expanded dramatically in Central America after WW2, increasing from 20,000 hectares to 463,000 in the late 1970s. This expansion was accompanied by a huge increase in industrial pesticide application which would eventually become the downfall of the industry.

The primary pest for cotton production, bol weevil, became increasingly resistant to chemical pesticides as they were applied each year. The application of pesticides also caused new pests to appear, such as leafworms, cotton aphids and whitefly, which in turn further fuelled increased application of pesticides.

The treadmill resulted in massive increases in pesticide applications: in the early years they were only applied a few times per season, but this application rose to up to 40 applications per season by the 1970s; accounting for over 50% of the costs of production in some regions.

The skyrocketing costs associated with increasing pesticide use were one of the key factors that led to the dramatic decline of the cotton industry in Central America: decreasing from its peak in the 1970s to less than 100,000 hectares in the 1990s. “In its wake, economic ruin and environmental devastation were left” as once thriving towns became ghost towns, and once fertile soils were wasted, eroded and abandoned (Lappe, 1998).

Sources: Douglas L. Murray (1994), Cultivating Crisis: The Human Cost of Pesticides in Latin America, pp35-41; Francis Moore Lappe et al (1998), World Hunger: 12 Myths, 2nd Edition, pp54-55.

Clone of REM 221 - Causal Loop diagramming

Ali Hamidi Toosi

A framework exploring flood risk management in a developing city, under the old paradigm of control - characterised by narrowly defined goals and an over-reliance on hard-engineered structural solutions.

Urban flood risk (control paradigm)

Michael Meadows

13

HANDY Model of Societal Collapse from Ecological Economics Paper

see also D Cunha's model at IM-15085 (Spanish)

Human and Nature Dynamics of Societal Inequality

Geoff McDonnell

19 12 months ago

A simple budget planning system. What additional complexities can you add?

ISD Savings Plan

Kevin Collins

126