Summary
This IOM and DG ECHO case study shows how regional pre-positioning, smarter freight choices, improved relief items and local procurement reduced supply chain emissions while maintaining operational readiness. The strongest result came from avoiding long-distance air freight, delivering a 66% reduction in transport emissions (649 tCO₂e) compared to the baseline, while ECO tarpaulins reduced emissions by 29% and waste by 55% compared with conventional tarpaulins, and packaging improvements cut packaging waste by 96%.💡The case provides a practical, evidence-based insights for logisticians to identify emission hotspots, prioritise high-impact actions and integrate sustainability into everyday supply chain decisions.
Content
Relief items often need to move fast, but speed can come with a high carbon cost when organisations rely on long-distance air freight.
This case study shows how IOM and DG ECHO reduced estimated emissions by pre-positioning stock closer to likely response locations, procuring improved relief items, and testing lower-carbon supply options. Freight transport delivered the strongest result: the Nairobi hub avoided an estimated 649 tonnes of CO2 equivalent emissions (tCO2e), a 66% reduction compared with routing the same freight through Dubai by air.
Even though procurement often creates the largest share of emissions in theory, in practice, changes to freight routes can sometimes deliver faster and larger savings. Freight changes may require fewer steps, while procurement improvements often need separate analysis and action for each product category.
The approach presented here gives humanitarian organisations and their logistics teams in particular a practical model for cutting emissions while protecting response speed, reliability and operational readiness.
OVERVIEW
The ReliefEU Stockpiles project pre-positions relief items on behalf of DG ECHO in Nairobi and Barbados. This analysis reviewed carbon emission and waste reduction for measures taking as part of IOM operations from November 2023 to May 2026, with a focus on the Nairobi hub where the strongest evidence is currently available. IOM’s Department of Humanitarian Response and Recovery (DHRR) worked with the IOM Environmental Unit and field mission colleagues to carry out the analysis, showing how cross-departmental collaboration can support practical emissions reduction work.
| Problem | Solution |
| Emergency stockpiles need to support fast response, but moving bulky relief items by air from distant hubs can create very high emissions. | IOM used a regional hub in Nairobi, prioritised road transport where feasible, procured improved ECO items (tarpaulins, sleeping mats, blankets), reduced avoidable packaging, and assessed local manufacturing opportunities. |
| Alternative items such as more durable tarpaulins exist, but it is difficult to estimate emission reduction with common emission factors since these don’t take into account e.g., emission benefits from durability. | IOM used a mix of emission factor and LCA data to estimate emission reduction from improved ECO tarpaulins. |
| It is challenging to estimate emission impacts depending on procurement location, e.g., items being manufactured in Kenya vs. China. | IOM used practical proxies such as energy grid mix for countries to estimate production location impact : The energy mix in Kenya has a higher share of renewable energy compared with China. |
Enabling factors: The project combined donor support, operational stock data, item-level emissions factors, and technical review to compare emission reductions with baseline scenarios.
Innovation
The analysis identified several emissions reduction opportunities, with freight providing the clearest and largest benefit.
- Freight changes delivered the biggest emissions saving: the Nairobi hub avoided an estimated 649 tCO2e or 66% for a shipment weight of 214 tons of relief items, compared with a Dubai-based air-freight baseline.
- ECO and recycled items reduced emissions as well: Improved, more durable tarpaulins, recycled sleeping mats and recycled blankets avoided on average an estimated 255 tCO2e or 29% counting a total of 42,435 items, compared with conventional items, assuming the recycled materials perform well in the field.
- ECO tarpaulins reduce waste. Using ECO tarpaulins with a unit weight of 4.56 kg also led to 47 tonnes or 55% of waste reduction compared to conventional tarpaulins.
- Local manufacturing helped: producing selected items in Kenya avoided an estimated 37 tCO2e or 17% compared to producing items in China or India, mainly because Kenya’s electricity grid has a higher renewable share than the comparison markets used in the analysis.
- Packaging changes had a smaller but useful effect: reducing single-use plastics avoided an estimated 5.9 tCO2e and about 1.9 tonnes or 96% of packaging waste, for a total of 12,635 kitchen sets and tarpaulin sheets.
- Hotspot analysis sharpened priorities: procurement emissions for Nairobi items totalled about 1,092 tCO2e; blankets, mobile storage units, sleeping mats and tarpaulins were among the highest-emitting item groups.
Results: Outcomes & Impacts
The case stands out because it looks beyond a single “green product” and compares emissions across freight, procurement, packaging and manufacturing location. This helps decision-makers see where action matters most: in this case, and even though in most humanitarian organisations’ carbon footprint procurement has the highest share of emissions, avoiding air freight delivered the largest estimated emissions savings.
IOM also found practical ways to estimate emissions despite limited data. To compare manufacturing and procurement locations, the team used each country’s electricity grid mix as a proxy. This means they assumed that an item made in a country with more renewable electricity would usually create fewer emissions than an item made in a country that relies more on fossil fuels. For example, an item produced in Kenya likely has fewer emissions than an item produced in China, since Kenya has a higher share of renewable energy in its electricity grid.
Spotlight: ECO tarpaulin sheets. The redesigned tarpaulin sheet generated estimated life-cycle savings of about 201 tCO2e or 49% savings compared with conventional tarpaulins. This measure also saved 54% or 47 tonnes of plastic waste compared with conventional tarpaulins. The savings came from a stronger, lighter design, a longer expected lifespan, lower transport weight, reusable bale packaging and local production in Kenya.
How was it achieved?
IOM built the analysis around actual stock movements and procurement records, then compared these results with practical baselines. The team used available emissions factors and life-cycle assessment data to estimate emissions from transport, items and packaging. The table below summarises the main steps.
| Preparation | Analysis | Implementation |
| Define the baseline, for example for freight: Compare actual Nairobi movements with a theoretical Dubai-based scenario using similar response lead times. | Calculate emissions for freight, procurement, packaging and selected local manufacturing using available activity data and recognised emissions factors. | Use the results to prioritise the highest-impact decisions: Avoid air freight where possible, focus on emission reduction of high-emitting items and test local production options. |
- Contact the WREC Coalition HelpDesk .
- Use carbon calculators such as the Humanitarian Carbon Calculator, available emission factor databases (see annex) and available life-cycle assessment data to estimate emissions from transport and relief items.
- Start with a hotspot analysis to identify which decisions have the highest potential impact before investing in detailed calculations.
Scalability – Can I do this too?
Other humanitarian organisations can apply the same approach by starting with the largest decisions in their own supply chains: hub location, freight mode, item specification and procurement source. The method works best when teams use actual movement and procurement data, compare it with a realistic baseline, and clearly state assumptions. It can scale across contexts, but organisations should avoid copying the figures directly because emissions savings depend on distances, freight mode, supplier location, item quality and operational constraints.
Lessons learnt
- Freight transport choices can matter most. The analysis shows that avoiding air freight can deliver larger savings than changing individual relief items. This is particularly interesting since on average, freight is responsible for 12% of emissions in the humanitarian sector – while procurement accounts for over 75% (see Climate Action Accelerator Roadmap for Halving Emissions in the Humanitarian Sector by 2030).
- Better product design is essential and works if durability holds in the field. Recycled and improved items can reduce emissions, but poor field performance could reduce or reverse the benefit. A tool for collecting and analysing field data on ECO items is currently missing.
- Packaging improvements help but rarely transform the footprint alone. Single-use plastic reductions are valuable, especially for waste, but if the aim is to reduce emissions teams need to also prioritise higher-emission hotspots.
- Production location needs careful analysis but makes a difference. Kenya’s cleaner electricity mix supported savings in this case, but a full life-cycle view should also consider raw materials, transport and supplier performance.
Recommendations
This analysis and actions undertaken provide practical recommendations for humanitarian logistics and environmental teams on reducing supply chain emissions while strengthening delivery:
- Start with a clear baseline. Define what you want to compare before you calculate savings. Use a realistic scenario, such as the current supply route, a likely alternative hub, or the usual transport mode.
- Identify the largest emission and waste hotspots. Review transport mode, hub location, item specifications and procurement source before spending time on smaller changes. These decisions often offer the greatest potential for emission reductions.
- Work with imperfect data. Humanitarian data is rarely complete. Use clear proxy data when needed, such as the share of renewable energy in a country’s electricity grid, to estimate how manufacturing location may affect product emissions.
- Cross-check which actions reduce emissions most in practice. Procurement often represents the largest share of emissions. But this doesn’t always mean the focus needs to be on procurement first. In practice, changes to freight routes can sometimes deliver faster and larger savings. Freight changes may require fewer steps, while procurement improvements often need separate analysis and action for each product category.
- Focus on practical options, not perfect scenarios. Test realistic alternatives that teams could use in a response, such as regional pre-positioning, road transport, local procurement or improved item specifications. This helps teams choose options that reduce emissions without weakening response speed or quality.
- Use the results to guide procurement and logistics decisions. Turn the analysis into simple decision points for future tenders, stock planning and transport choices. This makes emission reduction part of routine supply chain planning.