The transportation and logistics industry is one of the main contributors to carbon dioxide (CO2) emission to the environment. As a mean to mitigate the impact of human activities on greenhouse gas emission, international targets have been defined and agreed upon. While there are many CO2 reduction measures for logistics service providers and transportation companies across the value chain (on engine, vehicle, logistics and economic system-level), it is pertinent to measure the effectiveness of each CO2 reduction measure and ultimately identify the most effective and cost efficient solutions to be employed.
The technology described herein is a big-data based tool developed to help logistics service providers and national authorities assess the effectiveness of CO2 reduction measures across the entire value-chain of the transportation and logistics industry such as converting to electric drivetrains on the engine-level, inclusion of tire pressure monitors on the vehicle-level to reducing empty running at the logistics level. This is a low-cost solution that allows fleet operators and national authorities to assess which measures demonstrate the best cost-benefit to reduce CO2 emissions from transportation operations.
The Multilevel Energy Optimization (MEO) is both a method and a software tool which can be used to assess CO2-reducing measures in the transportation sector. By quantifying the effect of various measures, MEO helps to compare different scenarios and enable decision-making for implementing carbon emission reduction policies and measures. Based on MEO, companies in the transportation sector can be advised on when to implement which measures to achieve the CO2-reduction targets (e.g. Paris-agreement) in a cost-efficient way.
This software tool is able to quantify and provide a breakdown of potential energy saving and benefit to the environment as a result of implementing various CO2 mitigating measures. The type of measures may range from improving vehicle’s aerodynamics, the use of advanced powertrain solutions to logistic measures such as moving a distribution center (DC), changing the number of DCs, or bundling of goods. MEO does not simply add the effects when several measures are applied, but takes into account the interaction between the different measures.
Potential end-users could be small-medium, large-sized fleet operators and national authorities. The technology provider is seeking partnership or potential licensees to further develop the technology into a full fledged solution.
Individual fleet operators stand to benefit by understanding the cost-benefit trade-off between various CO2-reduction measures. At the aggregate level, MEO can support governments to devise incentive/penalty schemes to force use of more sustainable transportation operations.