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My final scenario has some similarity with scenario 1. I considered how each model re- sponds to an external positive event. In this case, I considered a government announcement to cancel their Hinkley Point construction plans several months after Fukushima. One of the reasons for considering this was to explore implementations of external positive events in each model, as originally they only considered negative events. Note that by events, I refer to world or UK incidents that are not directly sourced from NGOs or the Partnership.

I was also able to consider how significantly each model responded to a mitigating effect, following a significant negative event (in this case Fukushima). I could then compare this with scenario 1 where I assumed Fukushima did not happen.

7.4.5.1 Public Acceptance

Similarly to Scenario 2, I only present results for Copeland and Allerdale. I assume that the announcement to cancel the Hinkley Point construction plans happened in June 2011, which would be 3 months after Fukushima. I implemented this positive event as a negative modifier to my usual government trust adjustments. This negative modifier was smaller than Fukushima’s modifier to represent the difference in significance of the two events. How- ever, there was an additional allowance that government trust can increase slightly from these events if there have only been positive events recently. My results for the DES model are shown in Figure 7.7.

The overall trend of my DES model followed what was seen in scenario 1 for Copeland. However, the mitigating effect of a positive event increased public support. This could be considered interesting, as the Hinkley Point cancellation was a smaller event than Fukushima, and yet had a more positive affect compared to when Fukushima was removed altogether. A similar trend can be seen in Allerdale. The average positive support exceeded levels seen in the data at the end of PSE3. While the support is dropping quickly at the end of the pro- cess, all three communities were positive or neutral about the proposal overall. However, support would drop should the process be extended further. My DES model also became more consistent due to the balancing affect of the two events. Including the Hinkley Point cancellation has restricted confidence intervals to similar levels seen in scenario 1 when the stochasticity introduced by Fukushima was removed altogether.

The mitigating effect of the Hinkley Point announcement can be seen from my SD model results in Figure 7.8. For all three of the communities, public support dropped quite quickly following Fukushima. However, once Hinkley Point had been cancelled, public support held steady until PSE3. Unlike my DES model, my SD model was slightly more negative due to this initial decline. Despite this, the results were still positive overall, and does show a similar (but more exaggerated) trend to my DES model. This was a clearer demonstra- tion that the significance of the Hinkley Point announcement was lower than the Fukushima event.

Figure 7.7: DES model results for Copeland (top) and Allerdale (bottom) when Hinkley Point construction plans were cancelled.

Figure 7.8: SD model results for Copeland (top) and Allerdale (bottom) when Hinkley Point construction plans were cancelle.

If I compare the results of my SD and DES models, it is clear that the sensitivity of my SD model separates them in terms of behaviour. Despite the different behaviour of my two models, each model displays similar trends and also arrives at comparable results for the end of the process. This further reinforces the suggestion that the modelling paradigm that should be used would heavily depend on the perceived sensitivity of the process to these changes (although the SD model can be constructed in a less sensitive manner).

7.4.5.2 Summary

My third scenario has perhaps been the most promising in showing similar responses from both modelling paradigms. In both cases, the Hinkley Point cancellation served as a strong mitigating factor following the Fukushima accident. In particular, the feedback effect from both models has propagated this mitigation factor and resulted in noticeably higher support at the end of the process for both models, and for all communities. When compared to my results from removing Fukushima entirely, this could suggest that the proper response to a significant negative event could be to provide a more positive response than if the negative event had never happened. Unfortunately, the Partnership did relatively little to

ease concerns stemming from the Fukushima accident. Had more been done then a more positive result may have been more likely.

To conclude, this scenario has certainly highlighted the importance of mitigating events that could reduce the public’s trust in the government. While my SD model was more sensitive, it still showed the same trends as my DES model. Despite this difference in sensitivity, both models (and modelling paradigms) could be considered suitable. However, care must be taken to ensure that model choice reflects the expected sensitivity of the real system through thorough validation steps. The improvement of consistency seen in my DES model is also a strong benefit for a stochastic model, and this does again suggest that my DES model is more robust to changes than my SD model.