Reference product: transport, freight, lorry 3.5-7.5 metric ton, EURO1 [metric ton*km]
Location: ZA - South Africa
This dataset represents the transport of one metric ton.km of freight in South Africa by a 3.5-7.5 metric ton lorry of Euro 1 emission standard. The dataset represents the entire transport life cycle, including the production and maintenance of the vehicle, the transportation of goods and the construction, operation, maintenance and disposal of the road pavement. The scope of the dataset is the operation of the vehicle, which has inputs of vehicle production, vehicle maintenance, and road pavement construction, maintenance and operation, via linking to global datasets (i.e. the equipment and infrastructure datasets are not South African-specific). The dataset represents the average 3.5-7.5 metric ton lorry of Euro 1 emission standard vehicle within South Africa and is therefore an average across both petrol and diesel vehicles (i.e. the dataset represents the average of the entire fleet and shows the fuel use, both in terms of petrol and diesel, for the movement of a tonne.km of freight).
The average freight load of a 3.5-7.5 metric ton lorry is 3.9 tonnes, with an average freight load factor of 50% (both values calculated from SATIM model (ERC, 2015), which used factors from the Road Freight Association's vehicle cost schedule and calibrated these values to the 2014 tonne.kms reported by the Department of Logistics at the University of Stellenbosch. The load factor accounts for all transport trips). The average gross vehicle mass (GVW) is 7.1 tonnes (calculated from freight loading and that freight accounts for 55% of GVW in a heavy commercial vehicle (DoT,2009).
The dataset is not parameterised and factors cannot be changed by users.
Fuel consumption and the primary emissions (nitrogen oxides, nitrous oxide, methane, non-methane hydrocarbons, ammonia, benzene and lead) are modelled based on information and assumptions on fleet mixes and usage of vehicles in different road and traffic situations. Basic emission data is taken from HBEFA 3.4. (INFRAS, 2017), with extensive weighted averaging of vehicles in different traffic situations performed (see report for parameters applied and data sources for road and traffic conditions in South Africa). Emission factors for sulphur dioxide and for carbon dioxide are not taken directly from HBEFA but are calculated using the carbon content and the sulphur content of diesel and petrol in South Africa (and the fuel consumption). Heavy metal emissions to air (other than lead) are extrapolated from "transport, freight, lorry 3.5-7.5 metric ton, EURO3" on the basis of diesel consumption and "transport, passenger car, large size, petrol, EURO3, GLO, 2012" on the basis of petrol consumption. Emissions arising from wear of tyres, brakes and road are calculated from the applicable global datasets ("treatment of tyre/brake/road wear emissions, lorry, GLO, 2013") on the basis of GVW and freight loading.
Vehicle production and maintenance is described in the individual global datasets. Vehicle demand per tonne.km is calculated based on the lifetime kilometric performance, taken from Spielmann et al., (2007) (540,000 km for a heavy commercial vehicle), and the average freight load. The average freight load is calculated from the SATIM model (ERC, 2015), weighted to account for the current commercial vehicle fleet in South Africa (eNaTIS, 2017). Road construction, maintenance and disposal, and road operation are described in the individual global datasets. Road demand per tonne.km is calculated based on weighted road usage of each vehicle type of the road network in South Africa (see report for calculations and data sources).
INFRAS (2017) Handbook Emission Factors for Road Transport (HBEFA) 3.4. INFRAS, Bern, Switzerland;
eNaTIS (2018) Vehicle population statistics for December 2017/January 2018, Electronic National Administration Traffic Information System (eNaTIS): Pretoria, SA.;
ERC (2015) South African TIMES Model (SATIM) Energy Research Centre - University of Cape Town: Cape Town, SA.;
Spielmann, M., Bauer, C., Dones, R. & Tuchschmid, M. (2007) Transport Services Data v2.0, ecoinvent association, Zurich, Switzerland.; DoT (2009) National Transport Master Plan: The Implications of Global Oil Depletion for Transport Systems in South Africa. Department of Transport. Pretoria, SA.
[This dataset has been generated using the system model "Allocation, cut-off by classification". A system model describes how activity datasets are linked to form product systems. The allocation cut-off system model subdivides multi-product activities by allocation, based on a physical properties, economic, mass or other properties. By-products of waste treatment processes are cut-off, as are all by-products classified as recyclable. Markets in this model include all activities in proportion to their current production volume.
Version 3 of the ecoinvent database offers three system models to choose from. For more information, please visit: https://www.ecoinvent.org/database/system-models-in-ecoinvent-3/system-models-in-ecoinvent-3.html)]
The dataset reflects current technology in that the calculations apply current/recent road infrastructure, commercial vehicle fleet and road freight data for South Africa. The calculations also reflect current fuel quality and emission standards in South Africa.