ITF Non-urban Freight Model overview
The ITF has developed and updated its freight model to estimate the impact of policy measures on freighttransport under different scenarios. The first versions of the model are described in Martinez et al. (2015), ITF(2016), and ITF (2020).
The ITF freight model assesses and provides scenario forecasts for freight flows around the globe. It is a fully
integrated multi-modal network model that assigns freight flows on all major transport modes (air, inland
waterways, maritime, rail, road) to specific routes, modes, and network links. The maritime freight includes theaccess and egress inland components, and the mode choice includes rail, road or inland waterway components.
Centroids, connected by network links, represent zones (countries or their administrative units) where goods are
consumed or produced.

Figure 1. Transport modes distinguished by the model

Source: ITF
The model was developed to estimate the impact of transport and economic policy measures (e.g., thedevelopment of new infrastructure networks, the alleviation of trade barriers), technological breakthroughs or
improvements (e.g., high capacity vehicles, energy transition of long-distance road freight) and environmental
measures (e.g., CO2 mitigation measures).
The most recent version of the ITF freight model integrates the (previously distinct) surface and international
freight models. International and domestic freight flows are calibrated on data on national freight transport
activity (in tonnes-kilometres, tkm) as reported by ITF member countries. Said data is also used to validate theroute assignment of freight flows. Trade projections in value terms stem from the OECD ENV-Linkages trademodel (OECD, 2021) and are converted into cargo weight (tonnes). These weight movements are then assigned
to an intermodal freight network that develops over time in line with scenario settings. These define
infrastructure availability, available services and related costs.
The current version of the model estimates freight transport activity for 20 commodities for all major transportmodes, including sea, road, rail, air and inland waterways. The underlying network contains 8 467 centroids,
where goods' consumption and production occur. Of these, 1 164 represent the origins and destinations (ODs)for international trade flows, and 7303 represent the ODs of domestic flows. Each of the 152 863 links of thenetwork is described by several attributes. These include length, capacity, travel time (incl. border crossing
times), and travel costs (per tkm).
The model framework can be found in Figure 2. Each of the components will be described in more detail in thefollowing sections.

Figure 2: ITF Non-urban Freight model framework

Source: ITF

Model inputs
The model requires inputs of four main categories: trade forecast data; network data for different modes;
economic, demographic and geographical data; and initial carbon intensity data by mode (Figure 2).
Trade forecast data originate from the OECD's ENV-Linkages Computable General Equilibrium (CGE) model. This
global economic model describes how economic activities are interlinked across several macroeconomic sectors
and regions. The model is built primarily on a database of national economies. An economic input-output table
underpins each region, usually obtained from national statistics agencies. World trade in the ENV-Linkages modelis based on a set of regional, bilateral flows. All flows are expressed in monetary terms, in constant USD, usingpurchasing power parities as exchange rates for national currencies. The model projects international trade flows
in values for 26 regions and 20 commodities up to 2060 (J. Chateau, Fontagné, Fouré, Johansson and Olaberría,
2014).
Network data is mainly based on open GIS data for different transport modes. The ITF has consolidated andintegrated various modal networks into a single routable freight network. For this purpose, networks of thedifferent modes were interconnected by introducing transport links between centroids and using data on
intermodal dwelling times. Each link in the network has several characteristics, including its length, capacity,maximum speed, cost, travel times, and border crossing time (where applicable). The costs were estimated basedon the network data taking into account distance- and time-based components.
Economic and demographic data include population UN-DESA (2022) and GDP data for regions (OECD, 2022)
associated with each centroid. The economic characteristics of each region also include data on the contributionof the main sectors of the economy to the GDP. The main sources for detailed regional accounts are the WorldBank open-data database (https://data.worldbank.org/) for single region countries, Eurostat for European
countries (https://ec.europa.eu/eurostat/web/national-accounts/regional-accounts) and national accounts forother world countries over 100 million inhabitants.

Finally, data on the emissions intensity of each mode, as well as their projected changes due to technologicaland logistical developments over time, come from the in-house fleet model.

Model outputs
The model provides ton-kilometres (tkm) and vehicle-kilometres (vkm) for each link and node in the freight
network, disaggregated by transport mode and by commodity type. As such, values can be generated for each
OD pair and for single or multi-modal routes. These, in turn, can be aggregated at different geographical scales
to provide information on the following:•

Freight volumes leaving or arriving at a centroid, country, region or total and the breakdown of theirdestinations/origins•

The modal split of the activity by country, region, or total•

Throughput for each port, airport, or border crossing

These results can be further expanded and enhanced in combination with the ITF fleet model. The demand
results above are combined with information on related CO2 intensities and technology pathways by mode toestimate transport emissions to 2050. In the case of road and rail, these coefficients and pathways vary by region,
while maritime and air values are considered uniform around the globe.
They key outputs from the combination of the freight and fleet model are then•Transport CO2 emissions by mode, country, and region.

•

Activity and Emissions by vehicle type and distance bin. I.e., the vehicle types used for trips of different
distances

These last outputs are particularly relevant as it allows us to test the viability and feasibility of new and upcoming
technologies and formulate policy recommendations to increase the potential benefits.

Model components
The model estimates freight activity separately at domestic and international levels, converging at the end
for the shared use of surface transport infrastructure in countries. It has five main components:
1. Spatial discretisation model

2. International freight model

3. Domestic freight model

4. Equilibrium assignment module

5. Outputs module
Once all the components are set, the model is computed sequentially, as presented in Figure 2.
The model is updated in 5-year intervals and makes scenario forecasts up to 2050, with an adapted runningtemplate for the years 2019, 2020 and 2022 to reproduce the effect of COVID on freight demand accurately.
Consequently, potential future changes to the underlying freight transport network must be accounted for. Thesemay take the form of updates to infrastructure availability, the capacity or speed on certain transport links, ortransport costs that may evolve over time, given technological changes. Such potential updates are included in
the model via scenarios variables that are updated in line with a 'calendar of development' of the scenario. The
model user can easily change those input parameters. For example, in the case of Europe, a detailed calendar
representing the TEN-T network 1 development, accounting for its attributes, has been implemented. Information
on European intermodal terminals, including their expected delay times, has also been incorporated 2.

1

Detailed information available at: <https://ec.europa.eu/transport/themes/infrastructure/ten-t_en>.

2

Detailed information available at : < http://www.intermodal-terminals.eu/database >.

Detailed model component descriptions
Spatial discretisation model (centroids)

This sub-model defines two sets of centroids: international freight centroids and domestic freight centroids.

International freight centroids

International freight centroids are used to discretise regional OD trade flows into larger
production/consumption centroids. The discretisation allows for a proper breakdown of the travel path used
for different types of products and leads to a better representation of actual freight flows.
An adapted set coverage algorithm was implemented to identify centroids based on a larger set of potential
centroids. These potential centroids are all global cities with a population of fewer than 300 000 people, as
identified by the United Nations in 2010 (2 539 cities) (UN, 2015). The objective function minimises thenumber of centroids under the constraints that only one centroid can exist within a 500km radius (in thesame country), while the total of the globe’s land surface has to be attributed to a centroid.

Figure 3. International freight centroids

Source: ITF
This algorithm was adapted for some regions where increased spatial detail was desired in view of potential
future studies to be carried out by the ITF. For the European Union, the adopted resolution level was the
NUTS3 level in regions where a Functional Urban Area (FUA) is present and NUTS2 for the others (i.e., each of
the NUTS3 or NUTS2 regions (where applicable) is represented by one centroid).
1 164 centroids were defined. Each centroid is named after the most representative city in the region that thecentroid represents.
The influence area of each centroid is computed based on a raster world surface map. Each raster cell is
assigned to a centroid based on its distance to the centroid, while cells within a country will always be
assigned to a centroid within that same country. Each centroid is characterised by population and GDP
indicators. Raster cells were linked to global population estimates (CIESIN - Columbia University, 2018) andraster-based information on GDP (Kummu, Taka and Guillaume, 2018) are used to estimate the population
and GDP.

Domestic freight centroids
Domestic freight centroids define the origins and destinations of domestic freight flows. These centroids are
estimated by a set coverage algorithm similar to the one described above. It uses raster-based GDP data (Kummu
et al., 2018) to identify the most representative raster cell within a 100km radius.

The model produces a total of 7303 domestic centroids distributed as follows: North Africa (22), Central Africa(1020), South Africa (482), Commonwealth of Independent States (CIS) + Mongolia (823), South America (968),
Central America and Caribbean (262), North America (845), ASEAN member countries (509), China (553), India(248), Japan and Korea (69), Oceania (249), Middle East (450), other Asian countries (70), European Union (452)and Other European countries and Turkey (145)).
As is the case with international freight centroids, the influence area of each domestic freight centroid iscomputed based on a raster world surface map. Population and GDP estimates are linked to each centroid
while again respecting country boundaries (each raster cell is assigned to a centroid that lies in the same
country as the raster cell, based on its distance to the centroid). Figure 3 provides an overview of thedomestic freight centroids that have been defined in ITF’s freight model.

Figure 4. Domestic freight centroids

Source: ITF

International freight modelThis model includes a global intermodal network sub-model, underlying international trade projections, aweight-value conversion sub-model, and a mode share sub-modelIntermodal global network model

One of the main contributions of ITF’s freight model is the consolidation and integration of different modal
networks into a single freight network. Box 1 provides an overview of the data sources that were used toestablish the respective transport network information in the model.

Box 1. Sources of information for global transport networks• Road network information stems from the Global Roads Open Access Data Set (gROADS)(http://sedac.ciesin.columbia.edu/data/set/groads-global-roads-open-access-v1)
andOpenStreetMap (www.openstreetmap.org). Only the first and second road networks are
considered (i.e. motorways, main roads and truck roads).
•

For the rail network, the model uses data from the Digital Chart of the World (DCW)(http://www.princeton.edu/~geolib/gis/dcw.html) project that is updated with data fromOpenStreetMap on rail lines and rail stations as intermodal points of connection between roadand rail.

•

Maritime routes are obtained from the Global Shipping Lane Network data of Oak Ridge
National
Labs
CTA
Transportation
NetworkGroup(http://wwwcta.ornl.gov/transnet/Intermodal_Network.html), which generates a routable network withactual travel times for different sea segments. This network is connected to ports based ondata from the latest World Port Index Database of the National Geospatial-Intelligence Agency(http://msi.nga.mil/NGAPortal/MSI.portal).
•

Commercial air links between international airports were integrated using data from the OpenFlights.org
database on airports, commercial airlinks and airline companies (www.OpenFlights.org).

•

Inland waterways around the world were collected from the DIVA-GIS project (https://www.divagis.org). Their navigability was assessed by specific information about the rivers and the sections that are
navigable.

•

Information on oil or gas pipelines was also obtained from OpenStreetMap (www.openstreetmap.org).

All the above transport networks were interconnected with road-based transport links (connectors) that connect
the centroids to the network but also interconnect the different transport infrastructure (e.g. road-rail, road/railports, etc.). In order to estimate travel times for the different types of transport infrastructure, as well as
dwelling times between transport modes, the model uses average speeds based on available information byregion. Border crossing times were estimated based on available datasets from TAD/OECD(http://www.oecd.org/trade/topics/trade-facilitation/).

The data sources provided in Box 1 were also used to establish transport costs for the different links. These
costs encompass a distance-based and a time-based cost component. The distance-based cost component foreach type of infrastructure is matched to a different unit value per region or country that takes into accountinfrastructure quality/performance, fuel/energy costs and labour costs in the transport sector.
The methodology for these calculations was derived from Tavasszy et al. (Tavasszy, Minderhoud, Perrin andNotteboom, 2011), where the authors establish a procedure to estimate indicators for the total costs for alink/network. The values obtained for specific countries were calibrated so to match reported mode shares bythese countries. The time-based cost component reflects the value of travel time. An average aggregate valueof time per hour and ton of 0.196 dollars/h.ton is derived from Tavasszy et al., 2011. Commodity-specificvalues of time are estimated in the mode choice model, where a distinct time-sensitiveness is obtained foreach commodity type and transport type (container-based cargo, dry bulk, liquid bulk, transport equipment –RoRo and general cargo - more details are provided in Table 2).
The network model computes the free flow shortest paths between all centroids for each transport mode (ifthe mode is available), generating inputs used in the main econometric models (weight-value model andmode share model) presented next. These inputs are:
• The cost, travel time and distance by mode to link each pair of centroids;•

The shortest paths between the centroids for each transport mode.

Underlying international trade projections

The underlying trade projections that are used as an input to the model are disaggregated into 26 world
regions. This level of resolution does not allow for estimating transport flows with precision as it does notallow a proper discretisation of the travel paths of different types of products. Therefore, the model
disaggregates the regional origin–destination (OD) trade flows into the set of production/consumption
centroids as defined in the spatial discretisation model. The disaggregation procedure assumes a
proportionality of trade to GDP and uses raster-based GDP and population information to disaggregate trade
estimates at the regional level. It matches this information for the base year (2019) if the information isavailable in the UN Comtrade (2022) dataset.The GDP used in the disaggregation of each commodity justconsiders the GDP created within the respective economic sector. This is determined as a national or regional

share (depending on data availability) of each economic sector multiplied by the estimated raster-based GDP.
In the GDP of the EU, the NUTS3 or NUTS2 disaggregation was used to estimate this share.
Growth projections of centroids are based on the growth rates at the country level obtained from the OECD
2013 Economic Projections (Jean Chateau, Dellink and Lanzi, 2014), as growth rates are available at a country
level only. The split of trade activity of centroids within the same country as source/destination of trade is
kept constant over time.
The resulting equation for the estimation of the trade flows between centroids (OD pairs) for each type ofcommodity is given by𝑦𝑦𝑦𝑦
𝐺𝐺𝐺𝐺𝐺𝐺𝑜𝑜 ∙ 𝑆𝑆𝑜𝑜𝑜𝑜(𝑘𝑘)𝐺𝐺𝐺𝐺𝐺𝐺𝑑𝑑𝑦𝑦𝑦𝑦(1)𝑇𝑇𝑜𝑜𝑜𝑜𝑜𝑜 = 𝑇𝑇𝑉𝑉𝑉𝑉𝑉𝑉 𝑉𝑉𝑦𝑦𝑦𝑦
𝐿𝐿∑𝑣𝑣=1 𝐺𝐺𝐺𝐺𝐺𝐺𝑣𝑣 ∙ 𝑆𝑆𝑣𝑣𝑣𝑣(𝑘𝑘) ∑𝑙𝑙=1
𝐺𝐺𝐺𝐺𝐺𝐺𝑙𝑙Where𝑦𝑦
𝑇𝑇𝑜𝑜𝑜𝑜𝑜𝑜 = trade values from centroid o to centroid d in year y for commodity k,𝑦𝑦
𝑇𝑇𝑉𝑉𝑉𝑉𝑉𝑉 = trade values from origin region V to destination region L in year y,
𝑆𝑆𝑜𝑜𝑜𝑜(𝑘𝑘) = share of GDP related with the same economic sector of commodity k in the region or countryof centroid o,o, d = origin and destination centroids,k = commodity k,
y = year of analysis,k = centroid that belongs to the origin region V,l = centroid that belongs to the destination region L.

Weight-value model for the international trade

The conversion of value units (dollars) into weight units (tons) of cargo was formulated as a Poisson
regression model. The model estimates the rate of value-to-weight conversion, using as offset variable thenatural logarithm of the trade value in million dollars and a panel term representing the sensitivity of thedifferent commodity types to transport costs. The selection of this regression method was based on theobservation of the statistical distribution of the sample that fitted better a discrete statistical distribution thancontinuous distributions, especially for low trade connections. The model equation is given by:(2)
𝐸𝐸(𝑦𝑦) = 𝐹𝐹 ∙ 𝑒𝑒𝑒𝑒𝑒𝑒(𝑎𝑎 + 𝑋𝑋𝑋𝑋) + 𝜀𝜀
where F is the exposition factor and X represents the systematic component of the Poisson regression model.
The model was calibrated using Eurostat and ECLAC exports data 3 provided in value and weight units andusing transport cost information (stemming from the network model, as discussed above). Also, geographicaland cultural variables were used to estimate the model parameters: binary variables for trade agreements,
land borders and for reflecting whether two countries use the same official language were introduced.
Moreover, economic profile variables were included to describe the trade relationship between countries andthe scale of trade intensity. These are• The GDP percentile of the origin country (𝑝𝑝% 𝐺𝐺𝐺𝐺𝐺𝐺𝑖𝑖 );

•

•
•
•

The GDP percentile of the destination country (𝑝𝑝% 𝐺𝐺𝐺𝐺𝐺𝐺𝑗𝑗 );The GDP per capita percentile of the origin country (𝑝𝑝% 𝐺𝐺𝐺𝐺𝐺𝐺 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑖𝑖 );The GDP per capita percentile of the destination country (𝑝𝑝% 𝐺𝐺𝐺𝐺𝐺𝐺 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑗𝑗 );The natural logarithm of the GDP per capita ratio between origin and destination countries
(𝑙𝑙𝑙𝑙

3

𝐺𝐺𝐺𝐺𝐺𝐺 𝑐𝑐𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑖𝑖

𝐺𝐺𝐺𝐺𝐺𝐺 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑗𝑗).

https://sgo-win12-we-e1.cepal.org/dcii/sigci/sigci.html

All the economic variables were defined using a relative order of countries in terms of percentiles instead of
their absolute values. This is to avoid any disproportional effect in the future relation of value to weight. Anychanges here are not expected to happen as it is neither assumed that products will become lighter for the
majority of the commodities, especially with regards to raw materials, nor that a disruption in the market will
significantly change the valuation of some commodities over others.
As a result, the values estimated by the model assume a stability of both the commodities’ valuation overtime and of how productivity indicators may impact them. Yet, the value-weight conversion of some energy
market-related products, such as crude oil, refined oil, gas and coal, will depend strongly on the price of thesecommodities over time. For this reason, the value-weight conversion of these commodities has been indexed
to the value forecast by IEA/OECD (IEA, 2018a) to ensure consistency in terms of the forecasted volume oftrade for the next decades.
The model was estimated by keeping the minimum effect of the cost log sum positive and all commodities
sensitive to costs. The minimum threshold value of 0.025 was estimated for the panel terms based on theobservations in the dataset. The log sum accounts for the generalised costs, incorporating the time-based anddistance-based cost terms.
Table 1 presents the calibration results for the model.

Table 1. Weight-value model calibration results
Parameter

Intercept
Economic profile of countries

𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐
Economic, geographical and cultural relations between countries

Cost commodity panel term 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑
Chemicals
Coal
Crude oilElectronics
Fishing
Food products
Forestry
Gas
Iron and SteelLivestock
Metal products
Non-Ferrous Metals
Non-metallic minerals
Other manufacturing
Other mining
Paper, pulp and print
Petroleum & coke
Rice and crops
TextilesTransport equipment

Coefficient-13.907p-value/ level ofsignificance
0.00

0.123
-0.074
-2.22
1.08
0.686

0.02
0.11
0.00
0.00
0.00

-0.406
0.133
0.114

0.00
0.01
0.03

- 0.136- 1.679- 0.395- 1.161- 0.392- 1.324- 0.244- 2.573- 2.335- 0.277- 1.187- 0.025- 1.233- 0.025- 0.025- 0.245- 1.147- 0.524- 0.195
0.025

0.12
0.00
0.00
0.00
0.01
0.00
0.01
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.06
-

(Scale)Pseudo-ρ2
Correl (y, )2

1
0.73
0.92

Source: ITF

The overall model fit is high and shows the ability of the model to predict the conversion of trade values into
trade volumes (in tons). The model performs well in reproducing market patterns. The trade agreement
variable reveals to be a relevant explanatory factor; more expensive goods are typically transported further
away. All the economic variables were found to be significant, presenting interesting relations for the weightvalue ratio. Coefficients for 𝑝𝑝% 𝐺𝐺𝐺𝐺𝐺𝐺𝑖𝑖 and 𝑝𝑝% 𝐺𝐺𝐺𝐺𝐺𝐺 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑗𝑗 have a positive sign, indicating that larger
economies tend to export larger weights/values, and wealthier destination countries tend to import larger
quantities. There is quite symmetrical behaviour in terms of the size of destination markets and their wealth,showing that for less developed countries, it is more expensive to access products in the market. When there
is a large difference in wealth between countries, the model predicts the import of higher-value goods to thewealthier country.
The cultural relation between countries (set as a binary value that takes the value one if the origin anddestination country have a common official language and/or had a colonial relation in the past) was also
found to be significant. Such country relations lead to relatively more exports of greater-value products.
Conversely, countries with a land border tend to export larger quantities due to lower export costs.
Regarding the sensitivity of commodity types to transport costs, it can be seen that bulk-type commodities(liquid or solid) tend to be quite sensitive to costs. For example, textiles and other manufacturing products areless sensitive to transport costs. The value obtained for electronic components is also quite high, showing that
trade volumes for electronic products are pretty sensitive to transport costs.

Mode share model for international freight

The mode share model for international freight flows (in tonnes) defines the transport mode used for trade
between any OD pair of centroids. The modes include air, rail, road, waterways and maritime transport. The
overall mode attributed to each trade connection represents the longest transport leg in a multi-modal trip
chain. All freight is typically shipped on multi-modal chains, especially as the first and last legs are usuallydifferent from the main mode of transport. These latter domestic components of international freight
movements are often unaccounted for in the literature. The ITF model does integrate these components: In
the case of maritime transport, the model distinguishes one of three access modes (rail, road or waterways,
while for the other non-road modes, the access mode is always assumed to be road.
The model uses a nested multinomial logit formulation, including a time commodity type panel term and atype of freight cost panel term. The mathematic formulation is given by:′(3)𝛽𝛽 + 𝑍𝑍𝑠𝑠′ 𝛼𝛼 + 𝜀𝜀𝑛𝑛𝑛𝑛
𝑈𝑈𝑛𝑛𝑛𝑛 = 𝑋𝑋𝑛𝑛𝑛𝑛

where 𝑍𝑍𝑠𝑠′ represents characteristics of the nests, and ε follows a generalised extreme value (GEV). 𝜀𝜀𝑛𝑛𝑛𝑛 have ajoint cumulative distribution function of error terms, which is defined by
𝜆𝜆𝑠𝑠

𝑆𝑆

𝐹𝐹�𝜀𝜀𝑛𝑛1 , 𝜀𝜀𝑛𝑛2 , … , 𝜀𝜀𝑛𝑛𝑛𝑛 � = 𝑒𝑒𝑒𝑒𝑒𝑒 �− � �� 𝑒𝑒 −𝜀𝜀𝑛𝑛𝑛𝑛/𝜆𝜆𝑠𝑠 � �
𝑠𝑠=1

𝑗𝑗𝑗𝑗𝐵𝐵𝑠𝑠(4)

Where 𝜆𝜆𝑠𝑠 represent the nesting parameter that characterises each nest belonging to S.
The model was calibrated using export data sets from Eurostat and ECLAC, which contain information on thevalue, weight and mode of transport for exports from the EU and Latin America to the rest of the world. For
each OD pair, we estimate the modal share in weight by commodity group. Data on travel times and distancesfor each mode were taken from the global network model at the centroid level. Two geographical andeconomic context binary variables were added: one describing if the pair of countries have a trade agreement
and the other for the existence of a land border. For every OD pair, available modes were identified (e.g. landconnectivity). Some commodity classes, such as coal and crude oil, cannot be shipped by air.

The dataset contained 17 427 observations, with an average weighted mode share in weight units of 19% forroad, 1% for air, 79% for sea and just 1% for rail. The calibrated model has the likelihood ratio index (pseudorho squared) ρ2= 0.64, showing very strong explanatory power of the mode choice. All explanatory variables
are statistically significant (see Table 2).

Table 2. Trade value international freight mode share calibration results
Variable
Coefficient
Mode specific constantAir-2.921Rail-2.024Road
0.150
Lambda (nest parameter)
0.754
Sea – access by road
0.963
Sea – access by rail
0.550
Sea – access by waterways
1.050
Waterways-0.801Time commodity panel (1 000 hours)
Chemicals-0.191
Coal-0.002
Crude oil
-0.153
Electronics-0.383
Fishing-0.097Food-0.305
Forestry-0.010Iron and Steel-0.014
Livestock-0.096
Metal products-0.393
Non-ferrous metals-0.112Non-metallic minerals-0.006
Other manufacturing-0.177
Other mining
0.000
Paper, pulp and print-0.045
Petroleum & coke
-0.003
Rice and crops-0.008
Textiles-0.008Transport equipment-0.102
Cost type panel term (1 million dollars)
Container-based
0.03
Dry bulk
0.06
General cargo
0.02
Liquid bulk
-0.07
Transport equipment (Ro-Ro)-0.02
Geopolitical variables (trade agreement effects – TA, land border – LB)
TA (rail and road)
1.33
LB rail
0.978
LB road
1.33

Robust t-test

Robust p-value-41.50-40.22
12.42
2.56
1.87
2.01
1.65
1.52

0.00
0.00
0.00
0.00
0.01
0.00
0.02
0.04

-6.14
-15.21-25.64-157.44-99.28-33.33-32.89-30.19-2.65-32.11-23.14-52.56-40.150.00-39.6-15.14-22.11-2.81-35.18

0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

-4.78
-10.24-5.38-13.25
-6.51

0.00
0.00
0.00
0.00
0.05

4.47
28.11
92.99

0.00
0.00
0.00

The results show a greater relationship between the sea alternative and low-value raw materials and nonperishable products. Transport-related variables present an interesting behaviour that clearly distinguishes
sea transportation from the other available options. While increases in travel time reduce the utility oftransporting by sea, cost aspects ensure the attractiveness of this mode. In general, the utility of a sea tradeconnection will depend on the balance between cost and travel time, often defined by specific sea routes
and/or possible connections. Sea routes requiring a significant detour from the direct link are less attractive.
Sea routes from Europe to Asia that do not use the Suez Canal are an example of such less attractive routes.

Air cargo has a very negative independent term due to the inability to send large volumes or because there
are security concerns for specific commodities (chemicals, rubber and plastic, refined oil, livestock, othermetals, other minerals, coal, iron and steel, crude oil, other mining, rice and crops and gas). The absence of
direct flights is also very penalising (increasing the travel time and reducing utility significantly). Road and rail
present similar utility behaviours. Yet, the alternative specific constant for rail is quite negative. This indicatesthat (although the cost for this mode is significantly lower than the one for road) its attractiveness is relatively
low due to operational requirements that entail large delays and reliability concerns. This is partly due to thelarge number of rail operators involved in cross-national rail shipping.
Other important elements are geopolitical variables:• Trade agreements between countries seem to favour land-based transport, indicating a potential
simplification of border crossings procedures;•

A land border between countries favours exports through road and rail. Yet, this is stronger forroad as the potential rail interoperability issues are still present.

Domestic freight model

Inversely to a traditional four steps model, the modelling of domestic freight does not follow the generation,
distribution and assignment sequence. As no trade estimates between the different regions and cities of any
one region are available, the model departs from the total freight activity estimation and follows agravitational model to understand how the total trade splits into an OD matrix between the domestic freight
centroids.
For each country, the gravitational impedance and the distance for each available mode among the domestic
freight centroids allow the estimation of an average domestic travel distance. The total domestic freightactivity (in tkm) is then divided by the average distance to obtain the average freight cargo weight. Thisweight can then be assigned to the network following the OD matrices and the shortest path between
domestic centroids for the different available modes.
The two main steps of this model are presented next.

Estimates of total surface freight activity

Total surface freight activity in ton-kilometres is estimated by country. This includes all movements by road,rail and inland waterways inside each country’s borders, encompassing transport of international anddomestic nature, plus urban freight transport.
A Poisson regression model was used and calibrated on sample data from 51 countries from 2010 to 2015
with 306 observations. Observations cover all major countries that correspond to more than 80% of theworld’s surface freight movements (in ton-kilometre). Data assessments and extensive tests showed thatusing a discrete statistical distribution is more suitable than using a continuous distribution (e.g. lognormal)
given the wide range of values and country behaviours. The natural logarithm of industrial- and agriculturerelated GDP shows a higher correlation with freight activity than GDP per se. Therefore, it was used as anoffset (or exposition factor) in the function. More than any other factor, this guides the trend and determines
the volume of transport in each country. Other variables are related to the countries’ geography, transport
networks, and socio-demographic and economic structure (Table 3). The resulting equation is given by:(5)
𝐸𝐸(𝑦𝑦) = 𝐹𝐹 ∙ 𝑒𝑒𝑒𝑒𝑒𝑒(𝑎𝑎 + 𝑋𝑋𝑋𝑋) + 𝜀𝜀

where F is the exposition factor and X represents the systematic component of the Poisson regression model.
Model calibration results show the number of factors that favour surface freight transport volumes. These
include a country’s size, the existence of large ports, the facts of being landlocked or having natural resources
rents and ore-metal exports as a relevant part of the GDP (>12%). Also, having a geographical location andtransport infrastructure that allows for transit plays an important role. Conversely, very high GDP per capita
and population densities tend to reduce activity, meaning that richer economies are less transport intensive.
Likewise, countries with higher densities show less freight transport activity, as there are only shorter
distances to cover.

Table 3. Total surface freight activity calibration results
Intercept

Parameter

Connectivity for transit (1 000 000 km)
Population density ( 1 000 inhabitants/sqm)
Arable land (1 000 000 sqm)
Dummy variables
Inland waterways in activity
Large ports (in the 90 percentile of ports by capacity)
Landlocked
Natural resources rents and ore/metal exports
Fast growing (above 5% GDP growth)
Large countries in area (>1 200 000 km2)
Very low GDP per capita (< 4000 USD)
Low GDP per capita (4000 – 20 000 USD)
Very high GDP per capita (> 40 000 USD)
(Scale)Pseudo-ρ2
Adjusted ρ2Country profiles

Coefficient-14.890z - value-22723.6

Significance
0.00

1.871
-1.0570.516

796.3-574.91323.1

0.00
0.00
0.00

0.214
0.191
0.438
0.249
0.192
0.702
0.304
0.364
- 0.316

522.9
385.4
640.5
668.8
552.5
1283.8358.3-493.0
568.8
1
0.94
0.99

0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

Note: Offset (parameter with coefficient 1) for Industrial and Agriculture related GDP. Source: ITF

All variables in the model are significant, and the pseudo-R squared has a high value of 0.94.
Data sources for freight movements include ITF’s surface freight database (ITF, 2017a), Eurostat, US DOT, andother national statistical agencies. GDP composition and natural resources intensity of the economy was
obtained from the World Bank database. Ports capacity comes from the data set developed for (ITF, 2016).
The connectivity for transit traffic is an indicator that measures the route-kilometres that can take place ineach country for movements between contiguous countries or countries that share the same trade
agreements, the global road network and centroids for international trade where used for this calculation.
Part of the activity estimated by this model is already allocated to the network since it comes frominternational trade. The global intermodal network does not include urban activity, so the total volumes ofurban freight per country are accounted for, including the estimates for emissions, but no mode choice or
network allocation is performed. The share of urban versus non-urban freight transport activity in eachcountry is obtained from the IEA’s MoMo database (IEA, 2018a) as default. This information is complemented
with ITF country survey information for member countries.
The activity that does not correspond to urban or international trade-related movements is subjected tomode-route choice using an all-or-nothing assignment procedure (see next section).

Mode route choice for domestic freight

Domestic freight activity (in tkm) is estimated in alignment with international freight activity estimates anddomestic freight weights (in t).
The shortest paths between all centroids within the same countries are computed for all existent surface modes(road, rail and waterways) considering their attributes (e.g. cost and travel time). A simple gravity with thefollowing formulation is applied:
𝑉𝑉𝑖𝑖𝑖𝑖 = 𝑝𝑝𝑖𝑖 ∗ 𝑃𝑃𝑖𝑖 ∗ 𝑞𝑞𝑗𝑗 ∗ 𝐶𝐶𝑗𝑗 ∗ 𝑓𝑓(𝑐𝑐𝑖𝑖𝑖𝑖 )(6)

𝑓𝑓�𝑐𝑐𝑖𝑖𝑖𝑖 � = 𝑒𝑒 −𝛽𝛽𝑐𝑐𝑖𝑖𝑖𝑖(7)

In this equation, 𝑉𝑉𝑖𝑖𝑖𝑖 denotes the transported freight from zone 𝑖𝑖 to zone 𝑗𝑗, while 𝑃𝑃𝑖𝑖 and 𝐶𝐶𝑗𝑗 are the reported
production and consumption of freight in the corresponding zones multiplied by zone-specific multiplication
factors s 𝑝𝑝𝑖𝑖 and 𝑞𝑞𝑗𝑗 .These are multiplied by function 𝑓𝑓 that determines the accessibility of the destination zone
based on the generalised cost between zones 𝑐𝑐𝑖𝑖𝑖𝑖 :In this function, 𝛽𝛽 is a sensitivity parameter that is estimated to be 0.00045, while cij defines the impedance
between centroids (i.e. the zones that they represent), taking into account travel times and costs. The model
generates a distribution of origin-destination flows by mode within the same country. The resulting matrix
allows estimating the average travel distance of a freight movement and converting the tkm into weight.
These flows (in weight terms) are assigned to the intermodal transport network model and are there subject
to potential congestion also stemming from international traffic.

Equilibrium assignment

The model uses an iterative equilibrium assignment procedure with travel time and cost updates at every
iteration (5 years). This also accounts for transport infrastructure updates in line with the ’calendar ofdevelopment’ that provides information on how infrastructure develops over time (e.g. accounting for TEN-T
networks for Europe) - in terms of availability and/or link attributes (speed and capacity). Some componentsof the network are also assessed in terms of capacity and resulting congestion. Port capacity and throughput
are also updated every iteration, using the information on planned port capacity expansions as reported at
individual porta- or in the form of regional average growth rates. Port-specific data has been obtained frommaritime studies (Drewry, 2013; OCS, 2012a, 2012b, 2012c).
At every iteration, freight transport activity by mode is assigned to the shortest/least-costly path (based onminimising the generalised cost). In the case of maritime shipments, a route choice model also considers theavailable .alternatives for port selection and transhipment options for every OD pair. The introduction of thisprocedure in the overall equilibrium assignment reduces the number of iterations required to converge. For
creating the routing alternatives between each pair of centroids, a shortest path algorithm is computed
between ports to generate the port-to-port segment(s) – consider both direct routing or indirect routing via atranshipment port). Transhipment ports are limited to large ports (more than 1 million TEUs of container
traffic capacity, assuming an increase of this threshold value of 3% per year) with more than 25% sparecapacity in the previous iteration.
The route and port choice algorithms use a path size logit model, which accounts for overlaps between
alternative routes and transport costs associated with each alternative. The basis of this model can be foundin (Bottom et al., 1999). The model is calibrated by minimising the difference between observed andmodelled port throughputs for more than 400 major ports in the world. A detailed description of the model
can be found in (Halim, Kwakkel and Tavasszy, 2016) and in (Tavasszy et al., 2011). The formal definition of thecost model is𝑪𝑪𝒓𝒓 = � 𝑨𝑨𝒑𝒑 + � 𝒄𝒄𝒍𝒍 + 𝜶𝜶 �� 𝑻𝑻𝒑𝒑 + � 𝒕𝒕𝒍𝒍 �
𝒑𝒑∈𝒓𝒓

𝒍𝒍∈𝒓𝒓

𝒑𝒑∈𝒓𝒓

(8)

𝒍𝒍∈𝒓𝒓

where
Cr = unit cost of route r from origin centroid to destination centroid (USD/Twenty-equivalent unit,TEU),
p = ports used by the route,l = links used by the route,
Ap = unit cost of transhipment at port p (USD/TEU),
cl = unit cost of transportation over link l (USD/TEU),
Tp = time spent during transhipment at port p (days/TEU),
tl = time spent during transportation over link l (days/TEU),
α = value of transport time (USD/day).
The following is the formal definition of the route choice model. The route probabilities are given by𝑒𝑒 −𝜇𝜇 (𝐶𝐶𝑟𝑟 +ln 𝑆𝑆𝑟𝑟 )𝑃𝑃𝑟𝑟= 𝐻𝐻∑ℎ=1 𝑒𝑒 −𝜇𝜇 (𝐶𝐶ℎ+ln 𝑆𝑆ℎ)while the path size overlap variable S is defined as(9)In Equations (8) and (9):𝑆𝑆𝑟𝑟 = �𝑎𝑎 ∈ 𝐿𝐿𝐿𝐿𝑟𝑟

Z𝑎𝑎 1
Z𝑟𝑟 N𝑎𝑎ℎ(10)

Pr = the choice probability of route r,
Cr = generalised costs of route r,
Ch = generalised costs of route h within the choice set,
CS = the choice set with multiple routes,
h = path indicator/index,
μ = logit scale parameter,a = link in route r,
Sr = degree of path overlap,
Lkr = set of links in route r,
Za = length of link a,
Zr = length of route r,
Nah = the number of times link a is found in alternative routes.
At every iteration, the equilibrium assignment produces an all-or-nothing assignment (subject to the travel
time and costs update at each assignment iteration) of all transport alternatives simultaneously. The modelruns until there is a convergence of travel costs of all alternative paths for the same OD pair. The model
typically converges after 5 to 10 iterations.

Calculation of the outputs
The model provides ton-kilometres (tkm) and vehicle-kilometres (vkm) for each link and centroid in the freight
network, disaggregated by transport mode and commodity type. This allows calculating the corresponding values
for different origin-destination pairs and routes. These outputs can be further aggregated at country and regional
levels.
CO2 emissions are estimated for each commodity either via transport activity in tkm or vkm, depending on
the mode. For road transport, CO2 is estimated via vkm, using specific load factors of trucks for the different
types of commodities. Base load factors, specific for each commodity type, were obtained from the USA
Commodity Flow Survey 2017 4 and Eurostat (the European Road Freight Transport (ERFT) survey).
Load factors change over time as operational improvements in freight transport are assumed to happen.
These will allow reducing empty vehicle kilometres and making better use of vehicle volumes (e.g. by better
packaging). For other modes, CO2 estimates are derived by tkm. Respective CO2 intensities per tkm wereobtained from:• ITF fleet model (road, rail and waterways)

4•

IMO (maritime freight) (Smith et al., 2014),•

ICAO (air cargo) (ICAO, 2018).

https://www.census.gov/programs-surveys/cfs.html

Implementing Outlook 2023 scenarios
The ITF models were designed and further updated to estimate and evaluate the impact of policy measures ontransport activity and related emissions under two main scenarios: Current Ambition and High Ambition.
The Current Ambition scenario builds on existing policies and commitments to estimate the current pathways oftransport demand and related emissions. In contrast, High Ambition targets more profound changes in thetransport sector on demand management (generation control and sustainable modal diversion) andtechnological breakthrough. It was built by the ITF in 2020 for the preparation of the ITF Transport Outlook 2021
(ITF, 2021), and adjusted for the ITF Transport Outlook 2023 (ITF, 2023a) since. It is the result of an international
survey on current transport policies and technology development implementation worldwide, filled by many
experts in the ITF network.
Table 4 shows the measures implemented in the model with target values by 2050 aggregated in two simplified
categories: Global North and Global South contexts. They include enhancement to the infrastructure, economic
instruments, regulatory instruments and measures to stimulate innovation and development.

Table 4. List of measures for non-urban freight transport and their implementation in the model
Type

Policy measures
Electric roadinfrastructure

EnhancementofInfrastructure

Consolidation
centres andplatform sharing
Carbon pricing

Economic
instruments

Regulatory
instruments

Congestion or
distance-based
charges
Fuel economy
standards – forvehicles and fuelOperation ofshared assets
Low-carbon fuels
Platooning

Stimulationof innovation
anddevelopment

AutonomousvehiclesSlow-steaming andSmart-steaming

Model
Update costs and the resulting CO2 emission
per vkm for road. This affects the value-weight
conversion and the modal split
This increases load factors in all modes (morethe high capacity), but also, the logistic costs
increase. This impacts mode choice and theconversion of tkm to vkm and reduces theconversion of value to weight
Adapt the cost of each mode to reflect the CO2
cost per km. Impacts mode choice and route
choiceRoad costs would update costs based onverified congestion at each road link. Impacts
mode choice and route choiceThis element may only impact the CO2 intensityof the affected modes and the conversion ofvkm to CO2 emissions
The adoption of shared assets allows theincrease of load factors in road-based modes.
This impacts mode choice and the conversionof tkm to vkm
This element may only impact the CO2 intensityof the affected modes and the conversion ofvkm to CO2 emissions
This is implemented by decreasing road costs
and load factors of trucks. This impacts mode
choice and the conversion of tkm to vkm
This is implemented by decreasing road costs.
This impacts mode choice and the conversionof tkm to vkm
Reduce the sea CO2 intensities per ton/km by
reducing energy consumption at lower speeds
and minimising wait times at ports. This
impacts mode choice and route choice

Global North

Global South

30% - 50% of
TKM

0% - 15% ofTKM

20%
improvement10%
improvement

500 to 750
dollars per
tonne

300 dollars per
tonne

6.2
cents/tonne.km

2.4
cents/tonne.km

ITF ‘High Ambition’ scenario
20%
improvement10%
improvement

ITF ‘High Ambition’ scenario
5%-20% of TKM

2%-5% of TKM

5%-20% of TKM

2%-5% of TKM

33% of TKM

20% of TKM

Carbon pricing
Carbon tax, green taxes and fuel taxes are different forms of taxes that can be applied to the aviation sector
and are connected with the use of fuel or the fuel-burn resulting emissions. The main difference between
these different forms of taxes is whether they are applied per litre of fuel or per tonne of CO2 emitted. It isalso important to note that fuel taxes for jet fuel are already in place in many parts of the world. Currently,
fuel tax measures can be applied unilaterally by a country for its domestic flights or with a bilateralagreement for international flights between two countries. The CO2 benefits of fuel or carbon taxes comemainly from demand reduction (ITF, 2023b).
Model implementation

This measure is introduced in the model by varying the unit cost for each mode. It can be based on the
additional cost per vkm or tkm (based on the estimated load factor by commodity) by mode when a carbon
tax is in place. This measure affects the mode choices and route choices. The implemented carbon pricing per
ton is based on the reference literature (Baranzini et al., 2017; Carbon Pricing Leadership Coalition, 2018).

Congestion or distance-based charges
Congestion pricing sets a price for road travel with the objective of reducing congestion and the time losses
and adverse environmental impacts that it entails. Congestion pricing can be variable (the price is fixed at
different levels during different time periods) or dynamic (the price changes in real-time according tomonitored traffic levels). Congestion pricing can also apply to specific city zones (e.g. in London), roads (e.g.
the cordon/ring road in Stockholm) or segments of urban highways (e.g., tolled express lanes in the US)(ITF, 2023b).
Road pricing/charging is an old instrument widely used as a scheme to fund specific infrastructure
realisation: the toll has to be paid by the users of the infrastructure (which can be a motorway, bridge,
tunnel, etc.). The infrastructure perimeter has to be clearly defined. In Europe, road pricing is subject toregulation and law for trucks (see the Eurovignette Directive) (ITF, 2023b).
Charges can reflect all the costs related to the infrastructure: construction, maintenance, operation, andall the externalities associated with its utilisation. Charges can vary depending on the category of vehicles:
light or heavy / number of axels, Euro Norm of Emission, or some other characteristics. The charge can also
vary according to the time of day/year in order to reflect congestion level (and associated externalities).
For distance-based charging, there can be a per kilometre charge only or a per km charge with an additionalbase charge. For decarbonising transport, what is most important is to apply variation according to theemissions of the vehicles. This needs an additional system (the vehicle needs a label, like Eurovignette or
Crit'air) to be put into place (ITF, 2023b).
Model implementation

This measure may be introduced by affecting the cost of road freight activity. This cost increase will affectmodal split but also route choice, as the trade between distance and time spent in travel may change.

Slow and smart steaming
Slow steaming is the practice of reducing maritime vehicle speeds. By operating ships at significantly slower
speeds than their maximum speed, less fuel is consumed. This results in reduced CO2 emissions. Slow
steaming has been widely adopted since 2007, mainly due to the increased fuel costs at the time. Different
ship types benefit differently from slow steaming (ITF, 2023b). Besides financial benefits, regulation of ship
speeds could also be used to encourage slow steaming. There is also a possibility of using fuel levies to
induce slow speeding through the increases in fuel prices (OECD/ITF, 2018).

Model implementation

This measure yields significantly reduced CO2 emissions. A speed reduction of 10% translates into an engine
power reduction of 27% (Faber, Thomas Huigen and Nelissen, 2017). Lower speeds are more effective if
ship design speeds of ships are also lowered (Lindstad, Asbjørnslett and Strømman, 2011). The only
potential adverse effect may result from the increased travel time that will affect the transport cost of somecommodities that may decide the change for faster transport alternatives (e.g., road and rail). This measure
has already been tested by (Halim et al., 2016).

Electric road infrastructure
Electrification of heavy-duty vehicles is more challenging than for their lighter counterparts due to theweight of the vehicle and the distances they cover. A battery sufficient to provide the necessary power islikely to have a significant impact on the possible payload for the vehicle, negatively impacting operational
efficiency. Electric road infrastructure would overcome this by providing dynamic charging to moving
vehicles. Studies suggest some level of uptake by 2050, but this will vary geographically. It will largely bedependent on the political will to decarbonise the sector and the available alternatives. Investment ofpublic funds will be required for the initial development of the network, as a minimum level of coverage
would need to be achieved before operators would consider electrifying their fleets. International cooperation for cross-border routes would also be required to ensure interoperability (ITF, 2019).
Model implementation

The impact of electric roads was implemented in the model by changing road costs based on IEA
assumptions for their New Policy Scenario (NPS) (IEA, 2018a), affecting modal choice. This measure leads
to a 37% reduction in carbon intensity in countries where the main network is equipped with electric roadinfrastructure. The level and timeline of implementation are differentiated by region. All relevant
assumptions are based on (Cambridge Econometrics, 2018; ITF, 2018; Kasten et al., 2016); (Kühnel et al.,2018; Schulte and Ny, 2018; Siemens, 2017).

Platooning
Platooning or platoons refers to convoys of semi-automated vehicle convoys linked via vehicle-to-vehiclecommunication systems and is regarded as a forerunner to autonomous trucks, as it is already in advanced
testing. In an expert survey carried out by the ITF, a majority of respondents estimated the cost reduction
potential of platooning at 10% or more. Truck platooning can decrease the wind drag of vehicles driving
closely packed in a column and thereby increase fuel efficiency. But its benefits are more associated withthe reduction of operational costs (ITF, 2019). In future, policy measures that increase the cost of operatingroad freight, such as distance-based charging and carbon tax, could encourage the uptake of platooning to
reduce costs.
Model implementation

This measure is implemented in the model as a reduction of carbon intensity per vkm for vehicle activity inthe main network of countries where this measure may be implemented. The implementation schedule ofsuch a measure varies by region and prevalence of highways in the overall road network of the country. A
proportion of vkms being operated under platooning leads to a reduction in the total CO2 intensity. The
reduction in CO2 intensity was 14%. This assumption was derived from previous work at the ITF (ITF, 2017b).

Autonomous vehicles
Autonomous vehicles are a highly disruptive technology that could bring substantial changes in lifestyles,
car ownership, travel patterns, land use, etc. Therefore, authorities and policy makers are facing the greatchallenge of designing standards, legislation and policies which could help to minimise risks, alleviate thenegative effects and amplify the positive ones.

Authorities should provide legal clarity regarding the rights, obligations and liabilities of autonomousvehicle developers, operators, owners and insurance companies. This will include conditions under whichdevelopers can test and market the vehicles, criminal and civil liability rules and legal status for all the
stakeholders, etc. (ITF 2015). On the operation side, the legislation will have to define the liability of drivers,
passengers and vehicles in different situations (such as physical damage to a person or property, personal
data leak or if a vehicle is used as a weapon of terror or for a crime), for an occupied and unoccupied
vehicle. The insurance structure and compensation in case of an accident should also be defined.
Finally, the authorities will have to internalise the costs of driving as much as possible and stimulate
individuals, companies and organisations to own and operate the vehicles in a way which would allow for
reducing both direct and external societal costs, encouraging sharing rather than individual use of thevehicles (ITF, 2019).
Model implementation

This measure is implemented in the model as a cost reduction for road freight activities, resulting in potentialchanges to mode shares in freight transport. It is assumed the reduction of the driver costs is greater than theincreases in cost stemming from the technology and the vehicle (reducing by around 45% of current values). This
implementation is assumed to differ by geographical region. Carbon intensity per vkm is estimated to decreaseby 14%. These assumptions were derived from (Bagloee, Tavana, Asadi and Oliver, 2016; ITF, 2015, 2017b, 2018;
NRC, 2010).

Operation of shared assets
Sharing assets, i.e. information flows, transport mechanisms or stocking spaces, can promote efficiency inresource management for logistic activities. One same enterprise, or several of them, can benefit from thissharing of assets. ICTs have only facilitated asset-sharing by decreasing information costs and providing
platforms where various actors can share their assets. From an environmental point of view, sharing assets
can increase logistic efficiencies, for instance, by increasing the occupancy rate of vehicles. Multimodality
towards less carbon-intensive modes is also a possibility. Ultimately, improvements can reduce the numberof trips required to deliver products, thus reducing the number of emissions linked to logistic activities.
Asset sharing can also bring additional benefits. Costs for enterprises can be reduced by increasing
efficiencies, for instance, by decreasing fuel consumption. Improvements linked to asset-sharing measures
will be dependent on the types of activities led by the enterprises that decide to share assets. As an
illustration, it will be harder to share transport assets between one enterprise transporting food andanother one transporting industrial goods because of the difference in stocking needs for these products.
Governments may need to consider appropriate competition regulation to facilitate such asset sharing andmay need to consider how such actions could be enabled (e.g., through digital platforms) (ITF, 2023b).
Model implementation

This measure is implemented by adapting the load factor for each commodity type in each travel mode.
The reference values used for different scenarios are based on the efficiency increases introduced by the
IEA MoMo model in the different scenarios (IEA-NPC and IEA-EV30@30) (IEA, 2018b).

Fuel economy standards – for vehicles and fuelA low carbon fuel standard (LCFS) is a market-based policy mechanism aiming to reduce the life-cycle
greenhouse gas (GHG) emission intensity of transportation fuels/energy vectors.
The mechanism is grounded on the definition of progressively tightened regulatory thresholds/limits forthe average life-cycle (i.e. including production, distribution and use) GHG emission intensity of transport
fuels/energy vectors (typically gasoline, diesel oil and jet kerosene) distributed by regulated parties(typically fuel suppliers and/or other entities that produce, import, distribute or sell transportation fuels).

Fuels with a carbon intensity that is lower than the regulated threshold generate credits; fuels with higher
GHG emission intensity generate deficits. In any given year, regulated parties need to have enough credits
to compensate for any deficits created by the sale of carbon-intensive fuels. Regulated entities can tradecredits and use credits banked from previous years to ensure they meet the policy requirements in any
given year.
Mandatory vehicle efficiency standards require newly registered vehicles to emit less tailpipe CO2 emissions
than a specified threshold value (usually specified in gCO2/km or similar) by a certain target date.
Alternatively, such standards may be expressed as fuel economy standards that require vehicles to surpassa certain fuel-efficiency value (usually provided in miles/gallon fuel or similar). A vehicle’s tailpipe CO2
emissions and fuel consumption are typically assessed in standardised laboratory vehicle test procedures(ITF, 2023b).
Model implementation

This measure is implemented in the model as a reduction of carbon intensity per vkm for the road sector,
with a calendar of development by vehicle type and region in the ITF fleet model.

Low-carbon fuels
The development and availability of low or zero-carbon fuels for use in long-haul freight would be theoutcome of multiple policy levers, including standards to create the need for an alternative, and likely
incentives for research and development and for the uptake of alternative fuels, which may currently bemore expensive than the existing fossil fuel. The adoption of low-carbon fuels would reduce the CO2
emitted per vehicle kilometre (vkm) (ITF, 2023b).
The development of low-carbon fuels for long distances can be encouraged through regulatory policy
measures such as fuel economy standards and fuel blending mandates or through reducing investment risks
for research and development in alternative fuels. A number of financial measures involve the public sector
assuming risks that would otherwise need to be borne by private investors. Key examples include (ITF, 2023b):• the availability of financing from public entities at low-interest rates;

•
•
•
•
•
•

loan guarantees, i.e., the obligation by the public sector to pay some or all the debt in the eventthe investor fails to pay;debt service reserves, i.e., deposits of cash to pay interest and principal payments in case aborrower fails to make scheduled payments;
subordinated debt, where a public agency agrees to allow a lower priority position than senior debt
holders (in case of default, senior debt holders are paid in full before other debt holders are repaid);
the provision of grants by public agencies to investors to preserve or reduce the market interest
rate of a loan;credit insurance products for bond financing, i.e. insurances agreeing to pay a bond in the eventthat a payment default occurs by the issuer; andPublic Private Partnerships (PPPs), where the commercial risk is shared amongst private partners
and the public sector.

Model implementation

This measure is introduced into the model by changing the costs for road-based transport activity based on
the IEA assumptions for their New Policy Scenario (IEA-NPS) (IEA, 2018a), thereby affecting modal choice.
This measure also leads to a reduction in carbon intensity. The level and timeline of adoption aredifferentiated by region. All these assumptions are based on (Cambridge Econometrics, 2018; ITF, 2018;
Kasten et al., 2016); (Kühnel et al., 2018; Schulte and Ny, 2018; Siemens, 2017).

Consolidation centres and platform sharing
Distribution centres allow for the consolidation of shipments from multiple suppliers and thereby optimise
the loading of trucks onwards from the centre, improving the efficiency of the supply chain. This results ina reduction in vehicle kilometres and kilometres of empty running. Consolidation centres can also facilitatemode shift as it favours the transport of high volumes along main corridors, and trucks are required only
for the first and last legs, thus reducing the vehicle kilometres operated on road.
Policies that encourage more efficient freight transport, such as distance-based charging and carbon taxes,
as well as access restrictions on trucks in cities, can encourage the use of distribution centres as they can
aid operators in reducing costs.
Model implementation

This measure affects the average load factors of surface transport modes (road and rail) that connect themain regional freight centres to urban consumption centres. It will also affect the average weight of, or
space used in, each truck. Moreover, the use of a common platform to organise transport and measureactivity has also been reported as a very cost-effective measure that companies are increasingly using (Liand Yu, 2017). The load factor gains are mainly linked with freight for typically less consolidated
manufacturing goods that operate in a peer-to-peer manner or individual logistics chains of companies (e.g.
textile and electronics). Load factor gains can be reflected in the conversion of tkms to vkms for eachcommodity type, and the load factor gain scenarios may vary by world region and target year. Values
implemented can be based on existing literature on the topic, e.g., (Ballot and Fontane, 2010; Ülkü, 2012).

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