2B: Modelling of the Cost Effectiveness of Decarbonisation Measures


This project aims to develop a decision support tool for road freight operators. This tool will allow users to determine the relative cost effectiveness of a range of measures to reduce carbon emissions from their transport fleets, and aid decisions where to focus resources to maximise the financial and environmental return on decarbonisation actions. 

Objectives

The specific objectives of this project will be:

(i) Review the range of road freight transport decarbonisation measures, and identif the interventions to be included in the tool.

(ii) Add a commercial dimension to the model in order to permit estimation of net present value (NPV) and payback period of each measure.

(iii) Develop training materials to accompany the model.

Project Tasks

  • Carry out a consultation with potential users to define design specifications for the tool. In particular, obtain feedback on data entry requirements, key functionalities and required outputs of the model.
  • Develop the structure of the model and supporting macros in order to incorporate the findings of primary research (interviews, trials and simulation) and secondary data on the costs of implementing the measures.
  • Build a stochastic element into the model to allow for future variability in the impact of the full range of interventions.

SRF Optimiser

Click here to find out more about the SRF Optimiser

Academic Impact

This project will improve understanding of the economics of road freight decarbonisation at conceptual, methodological and empirical levels.  It will undertake innovative research on the integrated modelling of the cost-effectiveness of a large number of inter-related measures.  The results will be disseminated through all the main academic channels, as well as professional and trade publications.  The tool  will also  used for teaching purposes to provide students and managers with an insight into the costs of cutting carbon in the logistics sector.

Commercial and Social Impact

The decision-support tool will show managers and policy makers how freight GHG emissions can be reduced.  Most of the measures yield economic as well as environmental benefit.  Use of the tool  will promote more informed and rational decision-making in this field at both micro- and macro-levels.

References

McKinnon, A.C. Development of a Decarbonisation Strategy for Logistics. in Proceedings of the Logistics Research Network conference. 2011. University of Southampton