The road towards a greener future for the logistics sector starts with measuring and calculating greenhouse gas (GHG) emissions in a consistent and harmonised way across the supply chain. At present, there are a variety of methodologies and reporting formats for carbon footprinting, but these are often not aligned and therefore cause inefficiencies.
The LEARN project seeks to standardise carbon footprint tracking at a global level. This aim will be achieved through the establishment of coordinated networks among industry, government and civil society to drive transparent and comparable GHG measurements across transport modes and transshipment centers.
The LEARN project builds upon the GLEC Framework (Global Logistics Emissions Council) which aims to establish a harmonised methodology and to fill the gaps between these different accounting methods. The data collected using this methodology would then enable businesses to take informed decisions to lower their carbon emissions and improve the overall efficiency of their operations.
LEARN final event
On 6-7 Februrary 2019, LEARN Partners will come together to present and seek feedback from participants on the results and lessons from the project. The event will take place in Brussels. For more information and to register, please follow this link.
LEARN - Logistics Emission Accounting and Reduction Network
Harmonised carbon emission accounting for a greener and more efficient supply chain
The road towards a greener future for the logistics sector starts with measuring and calculating greenhouse gas (GHG) emissions in a consistent and harmonised way across the supply chain. At present, there are a variety of methodologies and reporting formats for carbon footprinting, but these are often not aligned and therefore cause inefficiencies.
The LEARN project seeks to standardise carbon footprint tracking at a global level. This aim will be achieved through the establishment of coordinated networks among industry, government and civil society to drive transparent and comparable GHG measurements across transport modes and transshipment centers.
The LEARN project builds upon the GLEC Framework (Global Logistics Emissions Council) which aims to establish a harmonised methodology and to fill the gaps between these different accounting methods. The data collected using this methodology would then enable businesses to take informed decisions to lower their carbon emissions and improve the overall efficiency of their operations.
LEARN final event
On 6-7 Februrary 2019, LEARN Partners will come together to present and seek feedback from participants on the results and lessons from the project. The event will take place in Brussels. For more information and to register, please follow this link.
IRU's role
IRU is raising awareness about the need for the road freight transport sector to adopt a standardised and consistent way of measuring and reporting carbon emissions.
IRU is participating in the development of training for companies on carbon accounting to respond to the needs of the road transport industry. IRU will also provide insights to develop practical guidance and policy recommendations for the reporting of the environmental performances of the sector.
A key role that IRU will play is to ensure that a harmonised carbon footprinting methodology will not translate into an excessive administrative burden for road transport operators in their daily operations.
Project results
Different tools to support companies in the process of measuring, reporting and verifying (MRV) emissions will be developed during the project, such as:
• Guidelines to implement the MRV methodology
• Training courses for carriers, logistics service providers and cargo owners
• Labelling to recognise the efforts of companies which will use the MRV methodology as described in the LEARN project
SFC, DLR, FHG, IVL, NTM, CONNEKT, CLECAT, TNO, ESC, EST, ZLC, IRU member UNTRR
Learn Project coordinator: Smart Freight Centre
LEARN Project website
For more information about this project, please contact:
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 723984
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