The standardization of major Well-to-Wheel models: measuring uncertainty on a macro level

This project concentrated upon the development of the Standardization Transport Model (STM) by assembling the largest possible assessment platform. It combines data from all of the major Well-to-Wheel (WtW) models in the field. The STM was developed for each chain under study by formulating the data in the major databases so that the Well-to-Tank processes covered Feedstock Production, Feedstock Transport, Fuel Production and Fuel Distribution. With the addition of Tank-to-Wheel data, a comprehensive STM was obtained for each chain. For each stage there is a range of values that was characterized by a probability distribution and through the use of Monte Carlo simulation the distribution was sampled and overall values for the total energy consumption, in MJ/km, and total GHG emissions in grams of carbon dioxide equivalent per kilometre (gCO2eq/km) were generated. By statistical means these distributions were compared to assess the risk of debt as well as the likelihood of major savings if they were to be implemented. The scope of the analysis was limited to passenger cars transport and does not include other forms of road transport.
Major classic WtW models may account for subjective uncertainty in the input parameters of the model but with a default set of inputs which represents only one database and one set of modelling assumptions and choices. This individualism and determinism in the WtW modelling nowadays explains the significant discrepancies that arise across the results from different models. The level of variation presented poses a major problem in the context of policy making and strategic planning. The generation of the STM rests upon the convection that a synthesis which generates a statistically relevant aggregate of the different WtW results from the different models of the major expert groups would eliminate the present inconsistencies and deliver the reliability required for making robust strategic decisions.
Advantage was taken of the richness of the STM outputs to assess the sensitivity of the results and identify the major factors of disagreement within the expert systems. Here the STM presents the largest platform of comparison and the most comprehensive evaluation of the different WtW models in the field. The provision of such a sensitivity analysis was not possible without allowing for variation in the elements of the model as done using the STM. Secondly, the key outputs of the model were compared under the criterion of sustainability from both energy and environmental perspectives. This was done by the synthesis of a first-of-its-kind distribution of the difference between the conventional system and the alternative system for each option under study. The output reflects as complete a population as possible of what may occur in reality in terms of direct impact on sustainability. This method of comparison was not possible without synthesizing an aggregate of possibilities as done using the STM. Thirdly, synergies with the power sector were studied to identify which strategies delay the global reduction in GHG emissions and which are to be preferred from an overall perspective. Here the author lead the transport research community in looking on the global benefits of alternative transport systems, rather than only looking through the window of the transport sector, by redrawing the boundary for the analysis of prospective transport systems. Last and not least, the outcomes of the comparative analyses of the STM results were aggregated into a proposed strategic framework for carbon and energy reduction in passenger cars transport. The strategic framework is placed into perspective by building a set of future scenarios and scaling the effect for the progressive implementation of these scenarios and making a comparison with the business-as-usual forecast.
The creation of an energy economy based on hydrogen fuel was found to be a highly questionable objective because electrically driven vehicles are superior with regard to systems that are either nuclear resourced or based on non-biomass renewables. For hydrogen, only the option from waste wood via gasification was found to be very attractive. However because only a minor role for hydrogen is foreseen, it is envisaged that the development of a hydrogen infrastructure would not be feasible. Therefore the use of hydrogen will be constrained to decentral systems or central systems with liquid hydrogen distribution. With regard to cultivated biomass, the sugar ethanol options are the best in terms of land use with sugarcane having the advantage of being economic and available for short-term penetration. The safe implementation of sugar ethanol, which includes avoidance of CO₂ emissions from indirect land use change and low fertilizers use, guarantees significant savings and have a good potential for large CO₂ emissions savings. Generally due to land use limitation cultivated biomass based options cannot be sustained on the long term. Last and not least, the CO2 emissions savings from clean coal technology is questionable without CCS technology and even though with the implementation of CCS no significant savings are certain.
On the other hand, besides the transport sector the power sector is another major sector of energy resource consumption and careful consideration of any synergies between the sectors is essential for the completeness of the analysis. The strategy in which the use of alternatives such as NG, nuclear and renewables is not diversified but fed only into the power sector is to be preferred as this avoids possible CO₂ emissions from indirect resource use change, and it also isolates the power market to maintain upstream energy security. Finally, the answer to whether it is still possible to save the World from the disastrous consequences of Global Warming is a preliminary "yes" but requires the development and implementation of a complete technology package including nuclear power which is widely debated at the present.

Keywords: uncertainty
environment
energy
Well-to-Wheel (WtW) analysis
transport
Advisor: Field, R