A few weeks ago, Steven Joyce spoke regarding traditional cost-benefit basis, here is what he said reported in NZ Herald:
Some projects in Auckland, such as the City Rail Link, did not stack up on a traditional cost-benefit basis, he said, although the Government has committed to funding for it.”
“I think there is unfinished business now for all of us to think about what are the true wider benefits of some of these projects and trying to get a bit more discipline to them in the years ahead,” he told the finance and expenditure committee at Parliament in response to questions from Greens co-leader James Shaw.”
“From my perspective, I think it is important that we go through the benefit-cost ratio discussion.”
“However I would signal that some of the projects that we collectively have all committed to, including the CRL, doesn’t really stack up on a traditional cost-benefit basis.”
Joyce is off the mark, however, the issue is not that we need better discipline but if whether the current way we calculate for business cases is even fit for purpose for the 21st century. It is telling that, after tens of billions of dollars spent on building roads over the past decade, congestion is now worse than ever. This suggests the current approach to prioritising really isn’t working and isn’t achieving its desired goals.
In times of discontinuity extrapolative models fail
One of the major issues current business cases have is they are heavily orientated around modelling. While modelling is a very useful tool we must also understand its weaknesses, one of the large weaknesses is inputs are based on historical data. This is great when we are moving along business as usual however in times of discontinuity these models fail. As we have seen, traditional modelling has on many occasions greatly under forecasted public transport demand. For example, recent work on improving rapid transit to the North Shore found the Busway is likely to start hitting constraints around a decade earlier than originally expected and could be much sooner if growth continues in the way it has recently.
Another classic example is the original business case for building Britomart in which just over 21k trips per day were expected to use the station by 2021, as of last year the number was at 42k.
This modelling under forecasting has wide implications for Auckland for two reasons:
- Lower passenger demand forecasts dramatically affect the benefit-cost ratios of PT projects as it understates the benefits it will provide.
- Under modelled forecasts result in PT step changes or implementation being funded to late.
Wider Economic Benefits are Sidelined
When discussing wider economic benefits, traditional engineers remark on how they are potentially subjective/hard to calculate & really should not be added or prioritised in business cases. However, this thinking is incorrect as we don’t build transport projects just for transport we build them because they enable aims like more housing, more economic growth, better environmental outcomes or a better city. Transport is always a means not an end. This type of thinking prejudices public transport/active modes whose real benefits go beyond just transport but enabling development as well as environmental/health outcomes. Also, systematically under-estimating wider economic impacts is like to lower the priority we give to projects that bring people together and maximise agglomeration (like the CRL) rather than those which encourage economic activity to disperse (like Puhoi-Warkworth).
For example, the City Rail Link business case had to account for the costs of all the land purchases but is not allowed to account for the sale/redevelopment of that land.
The Models are not as Scientific as people think
When people discuss models many people assume they are like they are in the natural sciences, however in transport this isn’t true as models in transport have many subjective assumptions built in which creates bias/perverse outcomes. For example, many models have assumptions such as traffic cannot fall even though the abundance of international empirical evidence of traffic evaporation & the Braess’s paradox such as Cheonggyecheon, Embarcadero Freeway, Utrecht, Paris, or even closer to home in Auckland with even the large amount of CRL works disruption. Congestion is lower than before the works on most streets.
This has a large effect on what we can do with the City Centre if traffic is assumed to not be able to fall or can only fall slightly then when asked can we re-prioritise street space the computer will always say no. Other assumptions such as large transfer penalties also effect business cases, resulting in lower modelled PT demand, even though we know given the right conditions people will transfer. Travel times normally include walking/cycling to and from PT stops, but not time spent walking to or from a car, or the time spent even finding a car park. Many assumptions are very predictive, for example the 2011 AWHC Business Case and the Ministry of Transport’s review of the first CRL business case both assumed parking prices in the CBD in the 2040s at rates lower than they are today.
Up until recently we assessed the benefits of infrastructure projects over just 30 years. This was not in line with other countries such as Australia who were using 40 years, which we have now moved to, or the UK which uses 60 years. This is important as imagine the increase in benefits in the business case for example if the CRL was assumed at 60 years of benefits rather than 30/40 years. For example, the Metropolitan line in London was completed 1863 and the original New York Subway Line completed 1904. While these have upgraded over the years, this infrastructure still in use today and more valuable than ever. While I am not suggesting we use 150-year timeframes it begs the question are we truly evaluating multi-generational infrastructure projects like the CRL.
Is a lack of diversity behind some of these assumptions?
The transport field is unfortunately not a very diverse one, this is a problem as diversity brings different perspectives/ideas. People plan what they know and if the field is heavily skewed towards a certain demographic, are we really surprised to find model assumptions that really only focus on peak commuting to work.
For example data from the US shows that commuting only makes up 20% of trips & a much smaller amount for women. While the data shows men travel more distance, women make more trips. Designing models simply around just peak commuting makes little sense, what outcomes would we get if instead, we focused modelling/designing for trips instead of the commute?
Sticking to discipline is pointless if the manual is not fit for purpose for the 21st century. What is more important is we realign our assumptions to international best practice.