Recolección de datos

The developed soft-linking framework embeds assumptions about the real world phe- nomena. The target system is the uptake of hydrogen in shipping taking into account

the supply of hydrogen in shipping and its use on board ships. The real world phenom- ena, therefore, is the interaction between the energy system that provides energy in the form of fuels to ships and the shipping system itself. If we assume that the dynamics of supply and demand of hydrogen in shipping in the real world are similar to the ones in the model TIAM-GloTraM, then it becomes important to highlight what are the as- sumptions that the developed linking procedure embeds about the interaction between the energy and the shipping systems.

A first assumption regards the two different assumed behaviours of the systems. The shipping system and the energy system act under different philosophical aims, which means that there are two different objective functions with two different scope. In other words, on one hand the energy system informs the shipping system of the fuels prices based on the least-cost abatement function, and on the other hand, the shipping system inform the energy system of fuels shares due to the investment that shipowners would adopt based on profit opportunity.

Another assumption regards the behaviour of the shipping system under a global decarbonisation scenario. In the case in which shipping carbon price is set equal to the global carbon price estimated in TIAM-UCL, essentially it means to include the shipping system within the optimal decarbonisation of the whole energy system. Because of this, the framework allows the shipping system to buy offsets for CO2 emissions from the rest

of the economy. So, in this case although the shipping system acts differently to the other sectors, it receives from the rest of the energy system the global carbon price that would be required if there will be a global effort to mitigate emissions. Alternately, setting the shipping carbon price equal to the one calculated in GloTraM means that the shipping system will act independently to meet its own CO2budget, while it is allowed to by offset

for CO2 emissions from the rest of the economy at a fixed proportion. Also in this case

the shipping system acts differently to the other sectors, however it sends back to the rest of the energy system the fuel shares that would meet the internal shipping emission target and the energy system will estimated fuel prices and global carbon price as it there will be a global effort to mitigate emissions. A possible alternative regarding the behaviour of the shipping system under a global energy decarbonisation scenario is that instead to have the assumptions that the rest of the energy system would act together, also the other sectors would act independently (based on different objective function) fighting each other to maintain a certain carbon budget that it would be proportional

Chapter 4. TIAM-GloTraM 106

to their relative contribution. This is not captured by the developed framework and it can be considered as a further area of research.

Results of the soft-linking

framework

5.1

Introduction to the results

This chapter addresses the first research question: how the soft-linking framework can improve the modelling representation of the uptake of hydrogen in shipping. To answer this question the results of key variables obtained with TIAM-GloTraM are compared with the ones obtained with independent runs of TIAM-UCL and GloTraM. The aim is to demonstrate that the soft-linking framework improves the modelling representation of hydrogen’s take-up in shipping by providing a more consistent set of results that enables the explanation of dynamics between the energy and the shipping systems that cannot be observed with independent simulations. If so, TIAM-GloTraM represents an improvement of the substance of modelling hydrogen in shipping in terms of its representational capacity of the target system, and it can be used to explore possible emergences in the energy-shipping system.

A total of six simulations were examined. Table 5.1 describes the six simulations. There are two reference scenarios: 4◦C and 2◦C. The first is a scenario that simulates the target system, ensuring that the average global temperature rise is below 4 ◦C, while the second is a scenario with a deep decarbonisation that ensures an average global temperature rise is below 2◦C. First, TIAM-UCL and GloTraM were independently used to simulate the two reference scenarios, then TIAM-GloTraM was used.

There are a large number of outputs from each simulation, however the focus is only

Chapter 5. Results of the soft-linking 108

Table 5.1: Simulations

Simulation name Description

IT4D Independent TIAM-UCL 4◦C IG4D Independent GloTraM 4◦C TG4D TIAM-GloTraM 4◦C

IT2D Independent TIAM-UCL 2◦C IG2D Independent GloTraM 2◦C TG2D TIAM-GloTraM 2◦C

on the key variables that are transferred between the two models; these are: the shipping transport demand of energy commodities, fuel and carbon price, the fuel consumptions and fuel shares mix. Such variables are not always outputs of the model used in each simulation, for example the trade of energy commodities is an output in IT4D and IT2D, while it is an input in IG4D and IG2D. Table 5.2 summarises such differences among the simulations.

Table 5.2: Variables examined

Variable Model

TIAM-UCL GloTraM TIAM-GloTRaM Trade of energy commodities Output Input Output

Fuel and carbon prices Output Input Output Fuel shares mix Output Output Output Fuel consumption Input Output Output

A simple comparison between these variables is unable to completely demonstrate that the results from TIAM-GloTraM are an improvement of the ones from the inde- pendent runs, because such variables are not all outputs of the model used. Every time a variable is an input then assumptions are required; for instance, fuel prices are out- put from TIAM-GloTraM and input in GloTraM. This means that when fuel mix are compared, it is difficult to demonstrate that one result is an improvement of the other results as the fuel prices input assumptions in GloTraM could have influenced the results and they cannot be associated with the representational capacity of the model. In other words, because of the different boundaries of the models, they cannot be used under the same exact assumptions, which prevents a fair comparison of the results themselves.

Even though the comparison of the results cannot demonstrate that TIAM-GloTraM can provide improved results, such a comparison is still very useful. The interpretation of the results for the variables in table5.2reveals a different level of insights depending on the model used. If TIAM-GloTraM is able to provide a more consistent and detailed

interpretation of its results, it demonstrates that the model is an improvement of the rep- resentation of the target system. The relationship between the modelling representation and interpretation is the focus. If my interpretation of the results can be more complete, detailed, and consistent then I should have a improved modelling representation of the system.

This chapter is organised as follows: section 5.2 and section 5.3 provide a general description of the key assumptions used for the two reference scenarios, including the assumptions required when the variables examined are inputs of the model used. Section

5.4 provides the results from the independent runs of TIAM-UCL for the 4◦C and 2◦C reference scenarios (IT4D and IT2D). Similarly, section 5.5 provides the results from independent runs of GloTraM (IG4D and IG2D). Then section5.6 provides the results from TIAM-GloTraM. Finally the comparison of the results and discussion are provided in section 5.7.