transport, freight, light commercial vehicle, EURO1, UPR, ecoinvent 3.6, Undefined

Category:
49:Land transport and transport via pipelines/492:Other land transport/4923:Freight transport by road
Location:
GLO - Global
Reference year: 2017 - 2017
Description

Location: GLO - Global
This global dataset is a direct copy of the South African dataset: transport, freight, light commercial vehicle, EURO1, ZA 2017. The dataset represents the transport of one tonne of freight in South Africa over a distance of one kilometer by a light commercial vehicle 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 light commercial vehicle 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 light commercial vehicle is 1.0 tonnes, with an average freight load factor of 75% (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 2.0 tonnes (calculated from freight loading and that freight accounts for 51% of GVW in a light 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 extrapolated from "transport, passenger car, large size, diesel, EURO3, GLO, 2012 on the basis of 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) (220,000 km for a light 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).
References:
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.
Undefined unit processes (UPRs) are the unlinked, multi-product activity datasets that form the basis for all of the system models available in the ecoinvent database. This is the way the datasets are obtained and entered into the database by the data providers. These activity datasets are useful for investigating the environmental impacts of a specific activity (gate-to-gate), without regard to its upstream or downstream impacts.

Technology

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.

Process type
Unit
Supported nomenclature
ecoinvent 3.6
LCI modeling approach
Before modeling
Multifunctional modeling
NONE
Format
ECOSPOLD2
Aggregation type
NOT_APPLICABLE
Data provider
ecoinvent Association
Review status
External
Cost
For sale
License

ecoinvent EULA

Contact
support@ecoinvent.org