Key Takeaways
- Global aviation emitted 1.03 billion tons of CO2 in 2023, representing 2.5% of total anthropogenic CO2 emissions
- Aircraft fuel burn efficiency improved by 25% from 2000 to 2022, saving 1.2 billion tons of CO2 cumulatively
- U.S. domestic aviation accounted for 2.1% of national GHG emissions in 2022, or 170 million tons CO2eq
- Maritime shipping emitted 1.05 billion tons CO2 in 2022, 3% of global total, with container ships at 40% share
- Slow steaming reduced shipping fuel use by 10% in 2023, avoiding 50 million tons CO2 amid high bunker prices
- LNG-fueled ships numbered 500 globally in 2023, cutting CO2 by 20% vs heavy fuel oil
- Public transit buses in U.S. cities carried 5 billion passengers in 2022, emitting 40 million tons CO2 total
- Bike-sharing systems globally provided 1 billion rides in 2023, avoiding 1.5 million tons CO2 vs car trips
- Electric buses in China numbered 600,000 by 2023, displacing 300 million tons diesel cumulatively
- Rail transport in the EU emitted only 0.4% of total GHG emissions in 2021, or 12 million tons CO2eq, despite 8% modal share
- High-speed rail in China carried 2.3 billion passengers in 2022, avoiding 320 million tons CO2 vs aviation/road alternatives
- U.S. freight rail efficiency: 479 ton-miles per gallon diesel, emitting 90% less CO2 than trucks per ton-mile
- In 2023, road transportation emitted 4.6 billion metric tons of CO2 globally, accounting for 24% of total energy-related CO2 emissions
- U.S. light-duty vehicles consumed 1.2 trillion liters of gasoline in 2022, contributing to 1.8 billion tons of CO2 emissions from road transport
- Heavy-duty trucks in Europe emitted 1.1 billion tons of CO2eq in 2021, representing 27% of the EU's transport emissions
Cleaner fuels, better efficiency, and electrification can sharply cut transportation emissions now and long term.
Aviation
Aviation Interpretation
Maritime Transportation
Maritime Transportation Interpretation
Public and Urban Mobility
Public and Urban Mobility Interpretation
Rail Transportation
Rail Transportation Interpretation
Road Transportation
Road Transportation Interpretation
How We Rate Confidence
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
Stefan Wendt. (2026, February 13). Sustainability In The Transportation Industry Statistics. Gitnux. https://gitnux.org/sustainability-in-the-transportation-industry-statistics
Stefan Wendt. "Sustainability In The Transportation Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/sustainability-in-the-transportation-industry-statistics.
Stefan Wendt. 2026. "Sustainability In The Transportation Industry Statistics." Gitnux. https://gitnux.org/sustainability-in-the-transportation-industry-statistics.
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