5. Results from club convergence analysis

It follows that varying administrative regions are likely to differ in their environmental goals and employ different economic strategies to achieve those. In our empirical analysis, we therefore expect regions to shift to a reliance on renewable energy sources at varying speeds.

Table 3 presents the first results of our club convergent analysis using the PS approach. We find that applying the club clustering algorithm to the data has led to the formation of four convergent clubs. The result of testing for full panel convergence among all the regions is also instructive. In the last row of Table 3, we can see that the log (t) statistics value for the entire sample is −46.832, which is less than the critical value of −1.65. The null hypothesis of full panel convergence is thus rejected at the 5 per cent significance level. This means that Australian regions have different transition paths in EV adoption. As this evidence indicates that the regions do not follow a single development path, it becomes conceivable to have a heterogeneous equilibrium with distinct outcomes.

Given that the club clustering algorithm may overestimate the number of clubs, we follow PS and apply a second round of club convergence analysis to the data to test the likelihood of initial clubs merging into larger clubs. This second classification exercise found no other clubs were merged in the process, hence the final number of clubs formed remains at four.

Table 3: Club convergence tests for electric vehicle adoption intensity

^{Notes: The analysis makes use of the critical value of t(p=0.05) = −1.65 across all cases in testing for the one-sided null hypothesis b ≥ 0 against b<0. Clubs represent the merged adjacent clubs.}

In Table 4, we present further results from the club-convergence analysis above. We can see that the first formed club consists of 42 regions from all jurisdictions except for one. NSW is well represented in Club 1 with thirteen of its regions included. VIC has nine of its seventeen regions included while QLD has eight of its eighteen regions included. Regions included in this first formed club are considered the most rapid and active EV adopters in the entire set of regions covered. At 42 out of the 87 regions in Club 1, this group of EV adopters comprise 48 per cent of the sample.

Table 4: The number of regions in each formed club, by jurisdiction

^{Notes:
a. Total captures the number of regions in Club 1 to total number of regions in each jurisdiction.
b. Total captures the number of regions in Club 1 and 2 to total number of regions in each jurisdiction.}

Also from Table 4, we can see that the second formed club consists of 33 regions out of the 45 remaining, and within this subgroup, there are 10 regions from NSW, eight regions from VIC and six regions from QLD. As members of Club 2 these 33 regions are considered the second most active and energetic regions in terms of EV adoption. The remaining 12 regions in the sample form the third and the fourth convergence clubs. The regions in this are the slowest EV adopters in the sample.

The first ‘Share’ column shows the rate of Club 1 membership in each jurisdiction. It is seen that ACT, TAS, VIC and WA have at least half of their regions belonging to Club 1, the club of fastest adapters. TAS scored the highest rating at 0.75 indicating that three of its four regions have adopted EVs relatively quickly in the last eight years. Furthermore, the fourth TAS region was found to belong to Club 2 – the club of second fastest adaptors – which implies that EV adoption rates in this ‘apple’ state were quite similar between regions and that no region in this state lagged. Meanwhile, it is seen that NSW just missed the 50 per cent cut off for Club 1 membership, with 48 per cent of its regions belonging to Club 1 while the rest identify with others in slower adaptors clubs. NSW sits alongside QLD and SA with ratings that are below 50 per cent which indicates a more uneven level of EV adoption within each jurisdiction¹ and increased chances of having lagging regions.

These trends are confirmed with the numbers found in the next ‘Share’ column which shows each state’s membership rate when Clubs 1 and 2 are combined. VIC and TAS lead the pack with all their regions found to belong to this combined club of fastest adaptors. Further, our club convergence analysis showed an absence of regions belonging to Clubs 3 or 4 indicating significant homogeneity in fast EV adoption behaviour within both states. In contrast, our results also showed that while close of half of regions in NSW are fast adaptors, 4 NSW regions or 15 per cent of regions in this most populous state lag in terms of EV adoption. Similarly, lagging regions in QLD and SA comprise 21 and 29 per cent of the state total, respectively.

A visual representation of our club convergence results can be found in the Appendix. As can be seen, regions that comprise Club 1 are found in urban centres of population with high‑density development for commercial and residential types. They include all Australian major cities, except Darwin in NT, as well as highly urbanised neighbouring regions. Our maps also show that regions belonging to Club 3 and Club 4 are located outside the city centres and tend to have low population density levels. These results were consistent with the expectation that the urbanised central regions within each state or territory will tend to lead others in terms of EV adoption. Conversely, they confirm that regions in the more remote locations and with very low population densities rates will tend to lag.

Collectively, our club convergence analysis strongly suggests the existence of lagging regions within Australian states, particularly for NSW, QLD and SA. This shows that EV adoption rates have been far from uniform within each state, and that state-level policies promoting adoption may need to be differentiated between urbanised centres and remote areas to ensure no region is left behind.

Footnotes

^{[1] We exclude ACT and NT here due to having just 2 regions in each one.}