This analysis projects vehicle miles traveled (VMT) for Grenada in the years 2020 and 2040 by simulating population growth, vehicle ownership, and urban travel distances between the two main urban centers (St. George and Grenville). We run 20 stochastic simulations per target year, sampling one of two speculative annual population growth rates (1.003 or 1.005) and using spatial centroids to compute inter-city travel distances. The output table lists VMT values for each simulation, and we report the mean projected VMT.

Understanding the factors that drive a country’s total CO₂ emissions is critical for designing effective climate policies. The Kaya identity decomposes emissions into four drivers—population, economic activity (GDP per capita), energy efficiency (energy per unit GDP), and emissions intensity (CO₂ per unit energy)—allowing us to quantify each factor’s relative influence on emissions trends. In this project, we apply the Kaya identity regression across multiple countries to answer: Which drivers most strongly explain cross-country differences in CO₂ output? How do population, wealth, energy intensity, and emissions efficiency vary in their impact? Are there notable outliers that deviate from expected efficiency patterns? By examining a global sample, we aim to uncover where policy interventions (e.g., improving energy efficiency or shifting fuel mixes) may yield the greatest emissions reductions.