This report is an addendum to the full NCCARF Settlements and Infrastructure project “Quantifying the cost of climate change impacts on local government assets” (See: http://www.nccarf.edu.au/publications/quantifying-cost-climate-change-impacts). Here we discuss the expansion of the road financial model developed in the full project to include additional local government locations across southern Australia. In addition, the
modification of the IPWEA designed NAMS.PLUS software to accommodate the outputs of the model are included. Outputs of this addendum project will enable the integration of the quantified impacts of climate change on local government sealed and unsealed roads into financial and asset management tools for 75 Councils across southern Australia.
Modifications to the model included altering the output to provide the annual average change in useful life for each asset class, a figure that can then be picked up by asset and financial management software. The outputs of the model have also been provided as look up tables for use in other asset management software for those Councils that do not use NAMS.PLUS.
The number of Local Government Areas in the model has also been expanded to include a total of 75 Councils. In keeping with the scope of the original project, all Council areas included are south of the 34oS latitude (Sydney). Composite areas for each of the capital cities were included (Sydney, Perth, Adelaide, Melbourne and Hobart) as well as other significant regional Councils including Ballarat, Hume, Bendigo, Gosford, Launceston, Victor Harbour, Kalgoorlie and Esperance.
The financial model was run for two future climate scenarios – a high emissions
scenario to the year 2100 for both the wet and dry end of the range as modelled by the Global Climate Models contained in OZCLIM. Standard values for each of the
additional variables to the model for each road asset class were selected from values provided in the case study Councils, the NAMS.PLUS data base or Rawlinson’s tables and are considered to be a fair representation of the average costs and conditions for a well maintained road. Results generated were included in lookup tables (Appendix 1) for input to the NAMS.PLUS model and for use by other asset management tools. As calculated in the full project and noted in the full report, the results indicate that, as the engineering formulas show, an increase in temperature and decrease in
precipitation is likely to improve road surface performance when all other factors affecting the useful life of a road are excluded. This result is indicated by small reductions in total whole of life cost and small increases in useful life of all three road types for each of the locations in the model. The range of effects is largest for the USR category and may reflect, in part, different methods of USR construction and different methods of data collection, both of which are more variable for USR. Also, although the impacts of climate change, though small, they are greatest for USR, because mean monthly precipitation is a direct driver of gravel loss; hence reductions in precipitation decrease gravel loss, as would be expected. AHR ranks second in the size of effect, followed by SSR. This result may reflect the differential binding of aggregates, sands and fillers under a warming temperature regime.
As can be seen from the look up tables, in all cases the calculated impact on the useful life of each of the tree road assets included in the model is small. However, it should be noted that for locations north of 34oS, a generally wetter climate and one that is
expected to get wetter as the climate warms, the results are expected to be different. As a result, the results generated by the model for these locations should not be
assumed to be an indication of expected changes nationally. In considering these results it is also important to note that:
1. The above simulation results are based on a real discount rate of 2.85%. Our selection of discount rate is based on the following considerations:
• Councils are different from commercial firms and they have low financial risk. Thus the discount rate should be close to the risk free rate as often
measured by the long-term government bond yield in Australia;
• over the period from 1971 to 2008, the average rate of return on 10-year government bond is 9.2% while the average inflation rate during the same period is 6.2% (these figures can be derived based on the data from Reserve Bank of Australia). Thus, the average real rates of return over the period is about 2.8%;
• the current interest rates (around 2010) in Australia are low compared to historical interest rates; and
• we evaluated the climate change impacts over a longer period of 90 years. Thus discount rate can never be precise and can be a contentious issue. If necessary, a sensitivity analysis can be carried out with respect to the discount rate. However, in our case, the consistent results from the field studies do not appear render it as necessary.
2. The model is based on inputs from climate data and engineering road
performance formulas and is therefore subject to the theoretical assumptions, data structures, and empirical testing of those areas. In addition, important assumptions have been made in the financial modelling in the way climate inputs are configured and in the simulations conducted.
Further, there are factors that have been identified as important, but which have not been incorporated fully in the modelling. For example, projected changes to extreme events are not well understood and not well captured in the climate data and so are not included in the model.
Finally, these results are based on changes in the climate only and so other factors that would affect the useful life of a road asset such as traffic volumes or usage are not included and yet would be expected to have a much greater impact on the maintenance costs of roads into the future.
In short, the cumulative effect of assumptions, across disciplines, in the financial modelling should not be underestimated.
In addition, to get a meaningful result from the climate modelling component in NAMS.PLUS, it is imperative that the asset register be an accurate record of actual engineering works in the field. It has been noted that the quality of Asset Registers nationally have improved from where that have been in the past, however, further improvement is required. The National Frameworks developed in 2008 indicate that data improvement should be a key target for Councils looking to move toward a ‘core’ level of asset management maturity. In theory, a Council that meets the requirement for core competency against the National Frameworks should be able to run the model and report the results in their Asset Management Plans if they so wish.
Finally, a step-by-step guide showing how to use the modified sections of the NAMS.PLUS software is provided in this addendum as Appendix 2.