Key Takeaways
- 10.4% reduction in logistics-related CO2 emissions potential with route optimization (IEA 2022 estimates)
- 2.0 Gt CO2e annual global emissions from logistics and transport-related activities are associated with freight transportation (IPCC AR6 WGIII, transport chapter)
- 40–60% of food loss and waste occurs in the distribution stage for food supply chains in middle-income countries (FAO)
- 74% of logistics companies indicate sustainability affects procurement decisions (DHL trend reporting)
- 45% of logistics executives say they face challenges in calculating Scope 3 emissions (Gartner)
- 72% of logistics decision-makers expect sustainability to influence contract awards (2024 survey)
- $6.2 billion global market size for sustainable logistics software in 2023 (MarketsandMarkets)
- Global spending on sustainable logistics software and services is projected to reach about $6.2B in 2023 (market sizing baseline)
- The global electric truck market is expected to reach about $34.5B by 2030 (forecast)
- 3.2% of global greenhouse-gas emissions are from shipping (2018 estimate)
- Fuel combustion accounts for 72% of total greenhouse-gas emissions from international shipping (2018)
- Light-duty vehicles account for 45% of transport CO2 emissions globally (2022)
- Freight transport demand growth is forecast to increase global freight tonne-kilometres by 60% between 2015 and 2050 (baseline from IEA 2019)
- The average warehouse productivity improved by 15% in 2023 measured as cases per labor hour among participating operators (2023 WHQ Benchmarking)
- The EU’s Energy Efficiency Directive requires a 2021–2030 binding target of at least 11.7% energy savings at EU level (2018/2023 framework)
Route optimization and efficiency can cut freight emissions and costs significantly, while sustainability reporting and software adoption accelerate change.
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Emissions & Decarbonization
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Industry Trends
Industry Trends Interpretation
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Market Size
Market Size Interpretation
Emissions & Energy
Emissions & Energy Interpretation
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Performance Metrics
Performance Metrics Interpretation
Regulation & Risk
Regulation & Risk Interpretation
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Energy & Emissions
Energy & Emissions Interpretation
Technology & Data
Technology & Data Interpretation
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Cost & Investment
Cost & Investment 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.
Gabrielle Fontaine. (2026, February 13). Sustainability In The Logistics Industry Statistics. Gitnux. https://gitnux.org/sustainability-in-the-logistics-industry-statistics
Gabrielle Fontaine. "Sustainability In The Logistics Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/sustainability-in-the-logistics-industry-statistics.
Gabrielle Fontaine. 2026. "Sustainability In The Logistics Industry Statistics." Gitnux. https://gitnux.org/sustainability-in-the-logistics-industry-statistics.
References
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- 5iea.org/reports/transport-sector-greenhouse-gas-emissions
- 6iea.org/reports/the-future-of-freight/energy-efficiency-and-technology
- 19iea.org/reports/shipping
- 21iea.org/data-and-statistics
- 24iea.org/reports/global-ev-outlook-2023/electric-freight-trucks
- 25iea.org/reports/global-transport-model
- 36iea.org/reports/the-future-of-trucking/energy-and-carbon-emissions
- 42iea.org/reports/global-ev-outlook-2024
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- 2ipcc.ch/report/ar6/wg3/chapter/chapter-7/
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- 27eur-lex.europa.eu/eli/dir/2018/2002/oj
- 28eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32008L0101
- 29eur-lex.europa.eu/eli/reg/2023/1805/oj
- 30eur-lex.europa.eu/eli/reg/2023/956/oj
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- 32eur-lex.europa.eu/eli/dir/2024/1760/oj
- 33legislation.gov.uk/ukpga/2008/27/section/1
- 34ecfr.gov/current/title-40/chapter-I/subchapter-C/part-98
- 35ecfr.gov/current/title-40/part-51/appendix-S
- 37ourworldindata.org/emissions-by-sector
- 38unctad.org/system/files/official-document/dtlstict2023d1_en.pdf
- 39fhwa.dot.gov/policyinformation/statistics/2022/vm1.cfm
- 40itf-oecd.org/sites/default/files/docs/zero-emission-heavy-duty-vehicles.pdf
- 41itf-oecd.org/sites/default/files/docs/net-zero-transport.pdf
- 44itf-oecd.org/sites/default/files/docs/urban-freight-transport-and-climate-change.pdf







