Data-Driven Policy Insights for Decarbonizing U.S. Aviation: A Multi-Dataset Analysis Using Python

This project integrates five key datasets to evaluate carbon emissions, fuel costs, subsidies, technological innovation, and carbon tax policy in the U.S. aviation sector. Using Python as the analytical engine, the project uncovers the relationship between government spending, emission levels, and the potential economic impact of introducing or expanding carbon taxation policies. Data Sources & Metrics Analyzed: Carbon Emissions US Civil Aviation emissions (2018–2022) in gigagrams Key trend: 37% drop in 2020 (pandemic) with slow recovery afterward Carbon Tax Policies (by State) California’s carbon tax reached $15.77/ton by 2019 Other states showed minimal or no carbon pricing activity Technological Advancements Aircraft engine specs including SFC (specific fuel consumption), thrust, bypass ratios Useful for assessing decarbonization potential through propulsion upgrades Fuel Consumption and Costs (Domestic vs International) Monthly breakdown from 2018 onward Cost-per-gallon trends used to evaluate economic viability of carbon pricing Federal & State Aviation Subsidies (2018–2023) Billions in annual funding tracked by state and year California and Alaska topped subsidy receipts, yet emission reductions varied Analytical Methods Used (All in Python): Data ingestion & wrangling: pandas, numpy Time series trend analysis and year-over-year comparisons Merging multi-source data (fuel cost + emissions + tax + subsidies) Visualization: matplotlib, seaborn for trend plots and heatmaps Carbon tax modeling: Simulated emission reductions using pricing elasticity assumptions Policy scenario simulation: Impact of a $50/ton national carbon tax on U.S. aviation Key Findings: Subsidies have not proportionally reduced emissions — many states receiving high funding still show inconsistent emission performance. Fuel costs alone do not discourage consumption — low cost-per-gallon correlates with high usage even in high-emission years. Technology investment (based on SFC and bypass ratio) shows promise in reducing long-term fuel dependency. A moderate national carbon tax could reduce aviation emissions by 12–20% over 5 years, assuming gradual elasticity and reinvestment. Skills and Impact Demonstrated: Advanced data merging and multi-source integration Environmental modeling using simulation and cost-emission dynamics Policy analytics: evaluating taxation vs. subsidization strategies Effective use of Python for climate-economic policy modeling Demonstrates how AI and data science can drive green aviation strategies