A Pareto analysis approach to assess relevant marginal CO2 footprint for petroleum products

• Refining LP models have limitations to provide useful marginal CO2 footprints.
• A multi objective optimization framework is developed to assess relevant CO2 data.
• Within a European Refinig industy, diesel is more CO2 intensve than gasoine.

Recently, linear programing (LP) models have been extended to track the marginal CO2 intensity of automotive fuels at the refinery gate. The obtained CO2 data are recommended for policy making because they capture the economic and environmental tensions as well as the processing effects related to oil products. However, they are proven to be extremely sensitive to small perturbations and therefore useless in practice. In this paper, we first investigate the theoretical reasons of this drawback. Then, we develop a multiple objective LP framework to assess relevant marginal CO2 footprints that preserve both defensibility and stability at a satisfactory level of acceptance. A case study illustrates this new methodology.