Brooklin is a small town that is developing quickly and has a younger average population than places like Whitby and Oshawa, therefore giving it a slightly higher birth rate. From when I was a child (Around 2008) it had a population of 15,000, and was estimated at >25,000 in 2018. This simple model is specifically for births and deaths, and doesn't take the fast development of neighbourhoods (i.e. people moving in from other places).

The dynamics of a codfish population with constant birth and death rates.

This population dynamics model simulates the growth and decline of the Javan Rhinoceros population over time (over course of 50 years). The model focuses on the balance between births and deaths within the population, providing insight into how different factors affect the species' survival and growth rates. By using constant fertility and death rates, the model reflects the natural dynamics of the Javan Rhinoceros population, illustrating the interplay between reproduction and mortality.

This model simulates the growth of human populations at a global level and a local level (e.g., Oshawa) using logistic growth principles. It includes components for population size, birth rates, death rates, migration for local population, and carrying capacity. Each stock and flow is described with units and explanations.

The model shows us how the human population within the city of Oshawa with density-dependent birth rate and the Immigration rate where the birth rate and immigration equals the death rate and emigration rate when the population is at carrying capacity whilst the birth rate and the immigration is greater than the death rate and emigration rate when the population is below carrying capacity, the birth rate and immigration is below the death rate and emigration rate when the population is above carrying capacity.

The dynamics of a moose population with constant birth and death rates.

Relation between factors contributing to carbon emission and carbon absorption

This complex system models the dynamics and impacts of transportation efficacy and efficiency of sustainable urban transportation in Durham Region. Within the Regional Municipality of Durham, there are eight local Municipalities consisting of Ajax, Brock, Clarington, Oshawa, Pickering, Scugog, Uxbridge, and Whitby, which account for a total population of approximately 750,000 individuals as of 2023. As Durham Region continues to expand and increase in population, road structures will be faced with an increasing traffic load that will generate a significant amount of carbon emissions. Due to the prevailing climate concerns, the need for sustainable urban transportation is evident.

Urban transportation systems are a cornerstone of city life. They connect communities and businesses, while providing access to services, supporting economic and social activities. Maintaining a vast network of transportation systems as cities grow, becomes increasingly challenging. These challenges, consisting of traffic congestion, environmental impact, and diverse transportation needs, become of paramount concern, such that political campaigns run solely on these platforms.

Within this stock and flow model are the main variables that comprise transportation emissions within Durham Region. The urban transportation model highlights different modes of transportation in the form of stocks, which consist of public transit users (bus), fossil fuel car drivers, and EV car drivers, as well as the inflows and outflows that are influenced by various variables. 

All data used within this model was obtained from the various sources on the internet. The data used within the model is based off of estimate values. Data pertaining to population values and cities/towns that comprise Durham Region, ON obtained from the following source(s): 

**NOTE: Kindly note we have used 10 sources in total that we have referenced - 2 are listed below as these 2 sources were used for the above description - the other references are located in the variables where the sources were used. 

Regional Municipality of Durham. (n.d.). Demographics and statistics. Durham Region Economic Development and Tourism. Retrieved November 24, 2024, from https://www.durham.ca/en/economic-development/invest-and-grow/demographics-and-statistics.aspx

Regional Municipality of Durham. (n.d.). Local municipalities. Durham Region. Retrieved from https://www.durham.ca/en/regional-government/local-municipalities.aspx

Group 8 Members: Leda Alizada (100821720), Pritika Lally (100867821), Mahad Rashid (100779108), Rileigh Rodych (100515185), Sami Siddique (100460897)

This model simulates electricity supply and demand in Waniaville, a fictional town with a population of 30,000. It captures inflow from the Ontario energy grid (sourced from nuclear, hydro, wind, solar, biofuel, and natural gas) and outflow to both residential and business consumers. The model also includes micro-generation from residential solar panels, showing how this reduces Waniaville’s dependence on the grid. The aim is to explore how changes in energy demand and solar adoption impact the town’s electricity consumption.

Plastic Pollution in the world. 

By: Roman Ahmad Zeia, Rhythm Alam, Hailey Uhlman-O’Conner, Ahmer Khan, Zain Siddiqui

This model estimates the annual emissions from Toronto's multiple transit options including Trains, Airplanes, Gas & Electric personal vehicles and Buses. The resulting chart reports in the in the amount of kilograms of emissions  over years.

This model simulates the global human population's growth and decline over a 50-year timeline, factoring in birth rates (density-dep), death rates, and Earth's carrying capacity (K). The model illustrates how population dynamics shift as the population approaches or moves away from K: when the population reaches K, the birth rate matches the death rate, causing no net change; when the population is below K, births exceed deaths, leading to growth; and when the population exceeds K, deaths surpass births, causing the population to shrink. Through this simulation, the model offers insights into possible population trends like stabilization, growth, or decline, and highlights the relationship between reproduction, mortality, and environmental limits. Clear units reflect these shifts per year.

Tanjiopolis is a unique municipality located in northern Canada, known for its extreme seasonal climate where there is six months of very hot summers followed by six months of very cold winters, with no transitional seasons. This distinct environment has driven Tanjiopolis to innovate and thrive, harnessing its natural resources to achieve energy independence.

The municipality has invested heavily in a robust infrastructure of solar and wind generators, complemented by a few nuclear power facilities. The nuclear plants operate at only 10% of their maximum capacity during the summer, as the abundant solar energy meets the municipality's power needs. In contrast, during the winter, the nuclear facilities ramp up to 100% capacity to compensate for the reduced solar output due to limited sunlight.

Tanjiopolis takes pride in its commitment to sustainability, reinforced by a government-mandated policy that requires 2 solar panels per residential building, 4 solar panels per small business building, and 6 solar panels per large business building. This ensures that the municipality can sustain a population of 3 million people entirely through renewable energy sources, maintaining a self-sufficient power grid that operates independently from external systems.

A model demonstrating the differences in productivity and cost in an arbitrary workforce (this could be one company/institution or an entire area). I used AI to assist me in creating this model, by explaining to it what I would like to have as my main stocks and what I'd like to model, and then had it suggest variables, flows, and links (and thus equations for the flows and variables as well). The actual initial values were also suggested by the AI.

The changing dynamics of distance and velocity over time according to several variables including, constant acceleration, constant mass, force and work. 

The dynamics of moose (prey) and wolf (predator) populations

The dynamics of Codfish (prey) and Shark (predator) populations. 

The model showcases a connected population growth of Codfish and Sharks in an ocean. The model has two positive feedback loops being the birth-rate of codfish and the birthrate of sharks; and two negative feedback loops being the death-rate of codfish and the death-rate of sharks. The stock components of the model link to each other through feedback loops. The feedback loops are the variables of codfish death-rate which depends on the stock of number of sharks, and the variable of shark’s birth-rate which depends on the stock of number of codfish in the ocean/model. 

In this model, I will be demonstrating my understanding of Modelling a Human Population by using Oshawa as a current example. For this model, we begin with a current population of 170,000. However, with Oshawa's "theoretically new" (just to demonstrate my understanding) neighbourhoods being built the population will change to reflect that and the city's appropriate carrying capacity! By using variable factors such as "Moving in/inflow rate" and "Moving out/outflow rate" to reflect the number of current residents residing within Oshawa (nResidents).

The changing dynamics of distance and velocity over time according to several variables including, constant acceleration, constant mass, force and work. 

Ayman Town is a fictional municipality located within Ontario, Canada. This model visually represents the balance between energy supply and demand, and consolidates energy from various sources—nuclear, hydroelectric, natural gas, solar, and wind—into a Total Power Supply that feeds into the town’s power grid. On the demand side, energy consumption is divided into residential and business sectors, factoring in micro-generation (e.g., solar panels) to calculate net demand, or the remaining load that must be met by the grid.

This model highlights the dynamics between traditional and renewable energy sources while showing how decentralized energy generation can reduce grid dependence. By understanding the flow of energy and identifying key drivers of demand, this framework helps guide infrastructure planning, energy policy, and sustainability efforts, ensuring the town’s energy needs are met efficiently and reliably.

The dynamics of the human population in Oshawa are influenced by the carrying capacity.

• When the population is below the carrying capacity, the birth rate exceeds the death rate, leading to population growth.

• At the carrying capacity, the birth rate equals the death rate, and the population stabilizes.

• If the population exceeds the carrying capacity, the death rate surpasses the birth rate, causing a population decline.

This model demonstrates how population growth is controlled by the availability of resources and space.

People (Human): Used for stocks like population and carrying capacity. It represents the number of individual humans.

People per person per year (Human / Human / Year): Used for rates like birth and death rates, indicating the number of people added or subtracted per person in the population each year.

People per year (Human / Year): Used for flows like population change, representing the total number of people added or subtracted from the population annually.