Climate Change and Population Growth

This project investigates the complex interplay between climate change and human population growth using a hybrid modeling approach:

a) System Dynamics Model (SDM): Designed a stock–flow framework with causal loop diagrams and equations modeling variables like radiative forcing, temperature rise, precipitation, soil quality, floods, droughts, food production, disease prevalence, birth/death rates, etc.

b) Agent-Based Model (ABM): Implemented in NetLogo to simulate individual agents characterized by age, health, fertility, and resource access. Agents interact with the environment and each other under climate-influenced conditions.

c) Numerical Methods: Integrated Euler’s and Runge–Kutta methods to solve differential equations; ran Monte Carlo simulations to capture variability and uncertainty across scenarios.

Outcomes:

1) SDM reveals long-term feedback loops affecting population dynamics and environmental conditions

2) ABM visualizes how individual-level behaviors under changing climate conditions translate into system-level outcomes

3) Comparative analysis showed how different approaches affect interpretations and robustness