Selection of low carbon technologies for heavy goods vehicles

Abstract

Profit margins in logistics are very tight and reducing fuel costs is critical to remain competitive. Customers and policy makers are becoming more sensitive towards climate change due to the links between fossil fuels and global warming. This research presents a framework to help decision makers to select the optimal heavy goods vehicles’ specification that minimises their carbon emissions cost-efficiently given their aversion to risk.The framework developed, uses a broad range of methodologies, techniques and tools including carbon emission lifecycle analysis; simulations; live trials; statistical analysis; metaheuristics and multicriteria decision analysis. An assessment of the waste-to-fuel opportunities of quick service restaurants showed that these could cover around 5% of the energy needs of UK commercial fleets and it was found that used cooking oil could reduce diesel emissions by over 85%. Among the range of scenarios built, the solution recommended by the framework indicated that all vehicles should fit spray reduction mudflaps, low rolling resistance tyres, automatic tyre monitoring systems and lightweight materials. While urban HGVs should also have automatic manual transmissions, regional and long-haul HGVs should include aerodynamic trailers and predictive cruise control instead. Compared to the do-nothing scenarios, the net present costs of urban, regional and long-haul vehicles can be reduced by 3%, 9.4% and 10.7% and their GHG emissions by 7%, 14.6% and 17.1%, respectively. This results in savings between £2.7M to £4.4M and 7,950 to 8,262 t CO2 eq. for the whole fleet of the industrial sponsor over 5 years. The lowest cost solution could save £5.8M and 27,684 t CO2 while carbon minimisation one could save over 30,000 tonnes and £2.9M, with current energy prices. The results suggest that diesel technology HGVs can still play a role in the decarbonisation of road haulage and that the uptake of low carbon technologies is highly influence by the risks aversion of the decision maker and duty cycle. The results demonstrate that the EU 2020 targets of delivering 10% savings from road transport by 2020 are perfectly feasible