Industry Issues, VTA

Carbon Reduction by Lowering Distance Travelled

Carbon Reduction by Lowering Distance Travelled

Explaining how to achieve a major carbon reduction by lowering distance travelled, was the task of Professor Russell Thompson is Professor of Transport Engineering and Department of Infrastructure Engineering at the University of Melbourne, speaking at the Alternative Fuel Summit, organised by the Victorian Transport Association.

As an example, Russell talked about a large urban area where they are distributing to a number of retail outlets across a metropolitan area. The example had 48 delivery points in the area and, typically, these deliveries would come from a number of depots warehousing to service these outlets.

The routes are quite efficient, independently, in their own right. However a more collaborative network with a compatible, integrated shipping concept, with transfers occurring between warehouses, with each warehouse receiving goods and then delivering all of them just within their immediate area, then running all the collections back to the local warehouse and later transferring the freight to the other other hubs in a consolidated form.

This shared network works out to creates a 78 per cent reduction in terms of transport distance travelled. This is gained just through the collaborative system. There are no new vehicles, no new facilities, the wholesalers and distributors simply agree to share the workload and achieve a significant reduction of 78 percent.

“This result is quite remarkable and and of course, the network efficiency gain is quite a lot higher,” said Russell. “This is very good news for trying to promote the collaborative network in terms of the scale. Those costs in terms of distance, translate to emissions, and financial cost savings as well. No new facilities, no new technology, no new vehicles.

“In logistics we’re quite often criticised because we want to build new terminals, we want to have more fancy trucks, but in this case, we improve by using existing networks, just with sharing and collaboration.

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“Similarly, if we’ve got a lot of freight moving across between our production and warehousing areas, a lot of the trips are returning back to the depot or back to the plant empty, and going quite considerable distances. So we looked at how we might be able to use our high productivity freight vehicles, and to look at local pickups and local drop offs with shuttles between these different areas. So we did the modelling, we did this for Melbourne’s key freight areas, and we were able to design a collaborative shared network consisting of hubs with high capacity freight vehicles running between them.

“We looked at the number of movements, the frequency and the volume that they’re traveling. We were able to get the kilometre tonne number down, to 27,000km travelled, down from 190,000km. This is an 85 per cent reduction again, just by sharing, collaboration, hubbing and using those high capacity freight vehicles.”

Quite dramatic savings can be achieved, and these equate to major emission reduction within the supply chain.. The key freight areas in Melbourne offer huge opportunities to reduce emissions. Also total cost of ownership is very important for the trucking industry, and the researchers did calculate these alternatives, the return on investment, the present values, the total cost of ownership, weighing up the variables, particularly with electric freight vehicles.

The number of variables make it difficult, no-one knows the price of energy in the future, whether electric or hydrogen or something else will dominate, or if any technology will do so. As a result, Russell is trying to work with mathematical methods which are able to encapsulate a lot of this variability, uncertainty, and can help the industry with its modelling, because it’s just so complex.

“We’re looking at a range of techniques, multi criteria analysis looks at different attributes for the different alternatives and compares them and looks at the different preference weighting,” said Russell. “So we’ve done a lot of work in multi criteria analysis. We’ve also done a lot of work in robust optimisation, which tries to determine the best solutions when you acknowledge the variability and you can actually develop scenarios to look into the future, and see what these are likely to be”

 

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