transport

Global freight transport emissions

The transportation of freight by land, sea and air underpins the complex network of global trade in physical commodities. Greenhouse gas emissions from freight transportation are a significant component of global emissions and are predicted to grow in coming decades. However, the inclusion of freight transport in emissions accounts and environmental impact studies is often incomplete. Both data availability and difficulties in allocating freight emissions to specific commodity trades contributes to this. In this study, international freight movements by transport mode are estimated from the bottom-up by imputing global freight transport routes. Emissions are estimated from these freight movements and integrated with a global multiregional input−output model. This enables the calculation of carbon footprints that are complete with respect to freight emissions. We estimate that global freight transport emissions contributed 2.8 Gt CO2-equiv in 2012, or about 41% of total transport emissions. In general, freight footprints contribute about 9% to national emissions footprints. While trade in physical commodities (such as construction materials, food and fossil fuels) are associated with the largest embodied freight emissions, services (such as public administration, education and health) also require significant freight transport. Using a consumption-based allocation of freight transport emissions allows the decarbonisation of other sectors to be complementary to the decarbonisation of transport through reduction in demand, for example through material efficiency strategies. To drive decarbonisation in maritime transport it is critical to include bunker emissions in national emissions inventories, thereby completing the system boundary.

Commodities with the largest contribution to the freight emissions footprint are construction (15%), food and beverages (12%), electrical products and machinery (10%) and petroleum, chemical and nonmetallic mineral products (9%) and agriculture (6%). The provision of construction services is associated with the largest embodied freight emissions. This is intuitive since construction supply chains require the transportation of heavy bulk commodities such as sand, gravel and timber. Furthermore, construction requires transformed materials that also require bulk transport in their supply chains, for example steel, concrete and masonry products. While petroleum, chemical and non- metallic mineral products contribute 9% to the total freight emissions footprint, the total embodied tonne-kilometres amount to 11% of the total. This can be explained through oil and other bulk commodities being transported by sea, with relatively low emissions intensity. Similarly, agriculture ranks higher in embodied freight emissions than embodied tonne- kilometres. This may reflect that agricultural supply chains are relatively simple, with products delivered to final consumption quickly after being produced. This contrasts with food and beverage products, which require more transformations and production steps, as well as freight transport. Education, health and public administration services are also associated with significant transport emissions. Despite being service sectors, these categories have complex material requirements which involve freight transport.

Previously, I discussed the construction of a global freight transport adjacency matrix here. This research was recently published here in the journal of Environmental Science & Technology.

 
 

Global freight network

Recently, I worked on a project to map global freight transport emissions. The transportation of freight by land, sea and air underpins the global trade in physical commodities. Greenhouse gas emissions from freight transportation are a significant component of global emissions, however the inclusion of freight transport in emissions accounts and environmental impact studies is often incomplete. Both data availability and the difficulty in allocating freight emissions to specific commodity trades contributes to this.

It is not possible to connect every origin-destination country pair directly using every mode, as some routes are infeasible. For example, a country may not have a seaport and therefore cannot trade directly by sea, or there may be no land border between two countries meaning direct road or rail connections are impossible. Furthermore, freight statistics contain the transport mode as it appears to the reporting country, i.e. the mode used to cross that country’s border. However, this record may represent only the first hop (for exports) or the final hop (for imports) of a multi-hop journey. For example, where a commodity is reported as arriving by sea from a partner country, it may have first travelled by road to an intermediate country with a sea port and then onward to the destination country by ship. To handle all these cases, multi-hop trade routes can be modelled between trading partners.

To perform this modelling of trade routes, an adjacency matrix is created that defines feasible direct connections between countries. A number of data sources are used to determine feasible direct trade, by mode, and the distance each route traverses. For sea freight, the WFP Geonode dataset of world sea ports is used to determine whether both origin and destination countries have sea ports and the sea distance between them given by the CERDI database. The CERDI database considers actual shipping routes between countries and shortcuts between landmasses, for example through the Panama Canal. For rail and road freight, countries must share a land border for overland routes to be feasible (with some exceptions, such as Denmark-Sweden, and Singapore-Malaysia). For rail freight adjacency, the additional condition is imposed that both origin and destination countries have internal rail networks. For air, road and rail freight the distance between the origin and destination capital cities is used (calculated using the Haversine distance between two points on a sphere). We assume a path exists between all countries with sea ports.

The adjacency matrix is available here: https://github.com/spottedquoll/cargo-journeys

Resources
WFP 2017, Global ports Geonode, World Food Programme, United Nations
Bertoli, S., Goujon, M., and Santoni, O. 2016, The CERDI-seadistance database
ITF, 2013, Key Transport Statistics, 2012 Data, International Transport Forum
World Bank, 2019, World Bank Open Data