These ideas stem from Gail Tverberg's blog: 'Our Finite World'. https://ourfiniteworld.com/
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.
parameters: s, alpha, delta, n, gA
variables: Y. K, L, C, A
per capita variables: y, k, c, a
per capita and technology variables: y~, k~, c~
steady state variables: y~*, k~*, c~*
all variables come with relative growth rates g
Features:
+steady state from beginning
+one time labor shock
+permanent savings quote shock
+permanent technological growth rate shock
Decreasing steady state variables when starting in steady state are numeric artifacts.
In summary, lower rates of consumption (based on production) result in higher rates of production and consumption in the long-run.
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=5118414432 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=5118418932 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 producao33 KB
Prova 8 Ponto deEquilibrio
ponto de eqilibrioibrio
32 KB
Prova 9:Analise de lucro e beneficios
Fluxo de caixa33 KB
The complex model reflects the COVID-19 outbreak in Burnie, Tasmania. The model explains how the COVID-19 outbreak will influence the government policies and economic impacts. The infected population will be based on how many susceptible, infected, and recovered individuals in Burnie. It influences the probability of infected population meeting with susceptible individuals.
The fatality rate will be influenced by the elderly population and pre-existing medical conditions. Even though individuals can recover from COVID-19 disease, some of them will have immunity loss and become part of the susceptible individuals, or they will be diagnosed with long term illnesses (mental and physical). Thus, these variables influence the number of confirmed cases in Burnie and the implementation of government policies.
The government policies depend on the confirmed COVID-19 cases. The government policies include business restrictions, lock down, vaccination and testing rate. These variables have negative impacts on the infection of COVID-19 disease. However, these policies have some negative effects on commercial industry and positive effects on e-commerce and medical industry. These businesses growth rate can influence the economic growth of Burnie with the economic
Most of the variables are adjustable with the slider provided below. They can be adjusted from 0 to 1, which illustrates the percentages associated with the specific variables. They can also be adjusted to three decimal points, i.e., from 0.1 to 0.001.
Assumptions
- The maximum
population of Burnie is 20000.
- The maximum
number of infected individuals is 100.
- Government
policies are triggered when the COVID-19 cases reach 10 or above.
- The government
policies include business restrictions, lock down, vaccination and testing
rates only. Other policies are not being considered under this model.
- The vaccination
policy implemented by the government is compulsory.
- The testing
rate is set by the government. The slider should not be changed unless the testing
rate is adjusted by the government.
- The
fatality rate is influenced by the elderly population and pre-existing medical
conditions only. Other factors are not being considered under this model.
- People who
recovered from COVID-19 disease will definitely suffer form immunity loss or any
other long term illnesses.
- Long term
illnesses include mental illnesses and physical illnesses only. Other illnesses
are not being considered under this model.
- Economic activities
are provided with an assumption value of 1000.
- The higher
the number of COVID-19 cases, the more negative impact they have on the economy
of Burnie.
Interesting Insights
A higher recovery rate can decrease the number of COVID-19 cases as well as the probability of infected population meeting with susceptible persons, but it takes longer for the economy to recover compared to a lower recovery rate. A higher recovery rate can generate a larger number of people diagnosed with long term illnesses.
Testing rate triggers multiple variables, such as government policies, positive cases, susceptible and infected individuals. A lower testing rate can decrease the COVID-19 confirmed cases, but it can increase the number of susceptible people. And a higher testing rate can trigger the implementation of government policies, thus decreasing the infection rate. As the testing rate has a strong correlation with the government policies, it can also influence the economy of Burnie.
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.
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 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.
Economic capital growth in a system constrained by a non-renewable resource, Figure 37 from Thinking in Systems by Donella H. Meadows
CLD exposition of Goodwin01 from Steve Keen's August 2019 course on Introduction to Economic Dynamics and Minsky software See video and powerpoint slides. Based on IM-2011 Minsky FIH and IM-168865 MacroEconomics CLDs. SeeIM-172005 for Simulation
In summary, lower fractional rates of consumption (based on production) result in higher levels of Savings.
