## 4 Results on Australian HSR Risks
### Survey Results
#### 4.1.2 Analysing Survey Data Using the AHP Method
The raw data were exported into Excel for further processing. Each participant's rating for a given question was multiplied by their respondent characteristic multiplier, as shown in Table 1. Then, a combined average was found. The process for answering any given question per respondent is illustrated in Table 2 below. This process ensured that responses were weighted according to expertise. After multiplying the raw ratings by the participant weightings, an overall average rating was calculated for each survey question. The above questions were also organised into a pairwise comparison matrix to evaluate their relative importance. The matrix was normalised, and priority weights were again calculated by averaging the rows of the normalised matrix. A finalised list of weighted questions was obtained and ranked from highest (most important) to lowest (least important). Table 3 presents the average weighted ratings for five randomly selected questions. \begin{table}
\begin{tabular}{l l} \hline \hline
**Item** & **Rating/Weighting** \\ \hline Respondent A Rating (Q) & 4 (out of 5) \\ Years of Experience of Respondent A (Y) = 21+ years & 0.558 \\ Field of Work of Respondent A (F) = Project Management & 0.633 \\ Major Projects Sector of Respondent A (P) = Rail & 0.418 \\ Weighted Rating = \(Q\times Y\times F\times P\) & 0.591 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sample survey response weighting example. In AHP, pairwise comparisons are used to assess the relative importance of different criteria or alternatives. These comparisons are subjective, meaning there is a risk of judgement inconsistency. The Consistency Ratio (CR) is a measure used to check the logical consistency of these judgments and ensure that the overall decision-making process is reliable [53]. A CR of less than 10% indicates that the inconsistencies are tolerable [56], and in this research, it was well below this threshold. Figure 7 presents the final list of barriers ranked on a scale from 0 to 1, where 0 represents the least essential barriers and 1 indicates the most important. To interpret the chart, focus on the rankings: for instance, \"Transport Shift\" received a weighting of 0.92, while \"Carbon Reduction\" received a weighting of 0.91. This means that the AHP analysis found that survey participants considered the impact of HSR on reducing car and plane trips (Transport Shift) to be slightly more important, placing it in the top 8 th percentile, whereas carbon reduction ranked lower in the top 9 th percentile. Although the difference in numerical weighting is minor, it suggests a subtle prioritisation of practical, mode-shifting outcomes over broader environmental benefits. This may reflect a view among stakeholders that encouraging behavioural change in transport usage is a more direct or actionable goal, whereas carbon reduction is perceived as a downstream effect rather than a primary driver in HSR decision-making. \begin{table}
\begin{tabular}{l l} \hline
**Survey Question** & **Average Rating** \\ \hline How likely is it that the construction of the HSR project will be delayed? & 0.223 \\ HSR projects require a greater-than-average risk contingency. & 0.215 \\ The high HSR development and construction cost is a barrier to & 0.213 \\ its implementation in Australia. & 0.222 \\ How likely will the HSR project be delivered over budget (cost overrun)? & 0.222 \\ The implementation of HSR will benefit Australia by increasing land coverage. & 0.174 \\ \hline \end{tabular}
\end{table} 
Table 3: Average weighted Likert scale rating for 5 random survey questions. Figure 7: Survey results ranked by AHP. The questions in the chart were given shorter labels for clarity. This analysis will be further discussed in the Discussion Section.