This model stimulates the growth of the human population at a large scale, ranging from global to local growth. It is modeled using logistic growth, where the carrying capacity (maximum sustainable population) limits the exponential growth due to available resources. 

The dynamics of homeless population in Toronto with constant homelessness and rehabilitation rates

This is the flow of electricity in homes and business of the fictional town Wilton. The values were based on the projected power plan of the town Milton. In this stimulation, we see how much electricity is coming in from each energy source and how the total energy on the town's power grid is distributed between residents and corporate buildings in Wilton.

This model graphs the general idea of the inflow and outflow of energy in a small town in Ontario's power grid. The main sources of energy come from Nuclear being one of Ontario's biggest sources of energy, Wind, and Solar. The graph shows energy generation vs energy consumption and the demand in energy in the town.

This model simulates human population growth on both a global scale and a local scale for Ajax, Ontario. The global population starts at 8 billion and Ajax starts at 121,780 Carrying capacities are set to 12 billion for Earth and 150,000 for Ajax. The model demonstrates the growth, showing how populations grow and stabilize as they approach their environmental limits.

Group 4:

Nami Zuha (100821467)

Zachary Wayne (100814747)

The purpose of the InsightMaker model is to model how Waste-to-Energy (WtE) technology impacts waste management efficiency, energy output and greenhouse gas emissions for the scale of ten years (assuming the WtE technology integration in an urban setting). This will determine if WtE has the capability of minimizing the reliance on waste landfills as well as assisting reaching renewable energy targets. This model will shine light on Waste-to-Energy sustainability opportunities and challenges.

Orange variables are associated with calculating waste volume, green variables are associated with calculating energy generation.

The power dynamics of a fictitious rural community (Bobville). The dynamics illustrate the power needs of Bobville residents and businesses throughout the day and how they change when microgeneration, residential/commercial daytime use, and nighttime use are considered.

This model simulates the management of water in a household. The “Water Reservoir” stock represents the total amount of water available. The “Water Supply” inflow adds water to the reservoir based on the “Supply Rate.” The “Water Usage” outflow removes water from the reservoir based on the “Usage Rate.” By adjusting the “Supply Rate” and “Usage Rate,” users can see how conservation efforts impact water availability over time.

This model displays how the population of the Earth changes. With a larger birth rate than death rate the population increases and heads towards K (carrying capacity). If the birth rate is lower than the death rate, the population will slowly diminish and will move away from carrying capacity.

The dynamics of a moose population with density-dependent birth rate...the birth rate equals the death rate when the population is at carrying capacity; the birth rate is greater than the death rate when the population is below carrying capacity; the birth rate is below the death rate when the population is above carrying capacity.

This model simulates the financial dynamics of an organic strawberry farm, illustrating how different variables impact the cumulative net income. Key inputs include the strawberry yield, sale price, worker hours, machine costs, and organic pesticide costs, all of which influence either the farm's revenue or expenses. The model allows adjustments to these variables, showing how factors like production levels, labor costs, and initial capital affect overall profitability. By visualizing these relationships, the model helps in exploring strategies to optimize income while managing costs effectively.

The following model shows us the fictional city in Ontario a municipality called Omnicity with fictional energy values and the relationship between all the energy types used within the city, how it affects the energy grid, with the inflows from the various types of energy the city produces. The power usage coming from Businesses and Residential, whilst the energy produced comes from Wind, Solar, Hydro, Nuclear, Natural Gas, and Micro-generated Solar Electricity from residential housing.

The UC Davis study gives yields for California, but the yields in Ontario are much less due to the less favourable growing conditions and season length (source:https://www.ontario.ca/page/dayneutral-strawberries). The revenue streams are different for the June-bearing and day-neutral strawberries depending on how many acres of each we have due to seasonal and off-season price differences and different yearly yields. The startup cost is the sum of the equipment, irrigation, and plant establishment costs, all of which depend on acres and set rates from the UC Davis study or the "Ontario Berry Crops Establishment and Production Costs 2022 Economic Report". The yearly expenses are labour costs, water cost, and fuel cost, which all depend on the number of acres and price rates for these aspects of the farm.

This model demonstrates the tradeoff between the resource costs of deploying AI systems—specifically electricity and water consumption—and the benefits gained from increased efficiency and effectiveness. It simulates how the deployment of AI systems grows over time and quantifies both the cumulative costs and the cumulative efficiency gains.
The following link will show how I created the model using AI:
https://chatgpt.com/share/671ff44a-912c-8008-b1ef-6535fe0ae0b1

This complex system models an organic strawberry farm in Northumberland County, ON. The model aims to highlight the various factors that influence revenue and expenses of the farm, and overall can predict the cumulative net income over a 25 year period. 

This model simulates a 100 acre organic strawberry farm. The net income of this farm is determined through several factors such as its yield and sales and its harvesting and packaging and labour as well. Adjusting the value of acres can increase farm size and see cost differences and net income differences.

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): 

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 explores the relationship between AI resource investment and the resulting gains in efficiency and effectiveness. The purpose is to illustrate how allocating resources to AI can drive improvements in performance but may also reach a point of diminishing returns.
The model helps users understand how different investment rates impact AI-driven improvements in efficiency and effectiveness.

By adjusting the Investment Rate and observing changes in Efficiency Benefit and Effectiveness Benefit, users can gain insight into the tradeoffs of AI investment, observing points where added investment yields smaller returns. This visualization is valuable for decision-makers seeking to optimize resource allocation for maximum performance.

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

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

This model shows how the population of Ajax, Ontario could change from 2026 to 2051. It starts with Ajax’s projected population of 140,960 people in 2026. The model looks at four main factors that can change the population: births, deaths, immigration, and emigration. Births and immigration add people to the population, while deaths and emigration decrease it. The rates in this model stay the same throughout the simulation and are based on general population data from Ontario. Since population can be affected by many different factors, this model is meant to give a simple estimate of how Ajax’s population could change over time.

The dynamics of a moose population with density-dependent birth rate...the birth rate equals the death rate when the population is at carrying capacity; the birth rate is greater than the death rate when the population is below carrying capacity; the birth rate is below the death rate when the population is above carrying capacity.