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Creating Common Measures for Logistics Carbon Emissions
Oct 19, 2017


By Susana Val

Managers can’t improve what they can’t measure. In logistics, the absence of a uniform measurement system coupled with a lack of knowledge about evaluating carbon footprints, makes it difficult to develop effective programs for reducing greenhouse gas emissions.
 
A project funded by the European Commission called The Logistics Emissions Accounting and Reduction Network (LEARN) is helping the industry to address these issues. The Zaragoza Logistics Center, Zaragoza, Spain is heading one of the project’s key components – training and education.

Paucity of data

Freight and logistics operations account for an estimated 7% of global carbon emissions. The carbon footprint of these activities must be reduced by at least 20% to meet the climate change goals agreed at the Paris Climate Agreement.

But to achieve such a reduction it is necessary to develop accurate – and, critically, commonly agreed – methods for measuring how much carbon is emitted in day-to-day logistics operations.

It’s a deceptively challenging goal in such a diverse and complex industry. If shippers, carriers and intermediaries approach the task differently, the result is a patchwork of measurements that is impossible to analyse in a meaningful way. Without meaningful data, the industry struggles to reduce emissions across end-to-end supply chains.

The scarcity of data also makes it difficult to show how carbon reduction programs can cut costs for logistics companies – an important incentive for joining such initiatives.

Bridging the gaps

There are 13 partner organizations in the LEARN consortium, representing a wide cross-section of logistics interests. The project’s aim is to empower companies to accelerate emissions measurement, reporting and verification (MRV) in transportation, thus enabling them to identify reduction actions and track their progress. These objectives are being pursued in four ways:

  • Provide support through guidance, training and education, and develop a blueprint for a labelling system.
  • Collaborate with companies to test and validate ways to apply MRV methodologies.
  • Promote and facilitate policies and research that supports the above mission.
  • Develop and involve a LEARN multi-stakeholder network to maximize business uptake of carbon accounting and reduction.

The LEARN partners are working with relevant organizations and initiatives to accomplish these tasks – including the Global Logistics Emissions Council (GLEC).

The GLEC is a group of companies, industry associations and programs that aims to apply supply chain carbon emissions accounting in a business environment. The overall vision is to drive emissions reduction and enhance efficiency across global supply chains. To achieve these goals, GLEC is harnessing the resources of its members to create a universal, transparent framework for calculating logistics emissions.

Although these efforts have moved the logistics industry closer to implementing such a framework, much work remains to be done.

For example, Europe’s road transportation industry is highly fragmented, with 75-80% of carriers employing fewer than five vehicles. Since current evaluation methods only partially address this fragmentation, there are disparities between key data inputs such as the type of actors involved, scope of the analyses, and the data sources used. Many companies restrict emissions measurements to their own operations, effectively ignoring other opportunities to shrink the logistics carbon footprint. Furthermore, road emissions standards are generally limited to covering the fuel/electricity used to power freight vehicles such as trucks, vans and cars as well as onboard systems such as cooling equipment.

Another challenge that has yet to be fully addressed is developing a holistic approach to the calculation and reporting of emissions levels. Logistics operations are extremely wide-ranging both functionally and geographically. To develop effective emissions control systems, organizations often need to manage processes that extend beyond their own operational boundaries. Companies, service providers and policymakers need to jointly develop innovative management information systems that collect data from multiple sources, integrate these inputs, and share the information with all relevant parties. These capabilities are gaining in importance as more data becomes available from rapidly expanding Internet of Things sensor networks.

The LEARN initiative is helping the logistics industry to meet these demands by working to refine and apply the GLEC framework in the real world, and to use this experience to develop version 2.0 of the framework.

Diverse models

Education and training is a vital part of this effort. Today’s complex supply chains encompass a broad diversity of business models and commercial agendas. Trading partners range from multi-nationals to small companies that operate in the sub-tiers of supply chains.

Large, multi-national organizations tend to be well versed in the need to measure and report carbon emissions. However, small to mid-sized enterprises often lack the resources and technical expertise required to create effective measurement systems. Also, many smaller players don’t participate in emissions reduction programs because they are not sufficiently aware of the wider supply chain benefits.

This is especially the case in Europe. Take, for example, the highly fragmented freight transportation sector. Many operators are unaware of carbon emissions MRV methods that are in common use. In addition, many have not established the management systems required to support data collection and reporting programs.

The ZLC role

As the leader of the LEARN education and training work package, ZLC is spearheading this vital part of the program in two ways: gathering data on the issues and disseminating relevant insights to stakeholders.

The data gathering phase began with a comprehensive industry survey. Launched in September 2017, the questionnaire has been translated into five languages and asks stakeholders about their carbon footprint initiatives, the polices they have established to support these programs, and their training needs.

The responses are still being collected and analysed, but some important preliminary results have been gleaned from the data. For example:

  • Most interviewees have no previous knowledge of emissions accounting tools or the GLEC framework. This observation is not unexpected, but still serves to affirm the need for education and training.
  • While respondents expressed interest in these tools – especially for cost-cutting purposes – a lack of government mandates and inadequate knowledge about applying the tools deter them from creating measurement initiatives. This could be one of the biggest barriers to emissions reduction programs.
  • The type of training needed varies with job function. Senior managers generally gravitate towards training in certification and reporting standards, while operational personnel prefer training on specific standards and policies. Environmental specialists support training on best practices for emissions reduction.
  • Functional differences are evident regarding respondents’ best practice preferences. Middle managers and senior managers prefer practices related to business strategy, whereas administrators, operations employees and environmental specialists lean towards training on specific emissions reduction measures. These results suggest that training programs need to be geared to different functional areas.

Informed choices

On completing the survey analysis, and adding information on existing training programs, ZLC will create a report on the findings that will be submitted to LEARN members for validation. The report should be available by the end of 2017, and efforts to put its findings into practice will begin in 2018.

Armed with this information, the logistics industry will be in a much better position to harmonize emissions measurement methods, and inform companies about the benefits of shrinking logistics carbon footprints.