As noted at the end of a prior post on innovative infrastructure-related innovation, a straightforward way to inspire more innovation in the uses of WIFIA loan benefits is simply to combine data sets.

The key elements are data about current and selected WIFIA projects (plenty of that online from EPA and local water systems), data that might be relevant to the affected communities (ranging from economic statistics to climate exposure – also widely available) and estimates of the financial value of WIFIA loan benefits connected to the project’s financing.

This last data set defines the funding that might be available for innovative initiatives, so it’s obviously the core element of inspiration for any realistic plan.  But estimates for the actual value of WIFIA loan benefits are not directly available.  Press releases for WIFIA financings often include simple totals of interest rate savings, but this is not the same as the type of value required to establish additional funding capacity, which is the real point.  In any case, press releases are (understandably) not going to follow a rigorous or consistent methodology in the numbers they report.

Fortunately, however, since the relevant WIFIA borrowers are all highly rated public water systems, there’s plenty of raw material from publicly available financial information to model WIFIA loan value estimates accurately, if not precisely.  A standardized model, with specific project and borrower data input for the variables, will provide a uniform and consistent approach.  It’s also a practical and efficient way to generate value numbers for the dozens of individual projects involved.  This part of the exercise, though it involves an extra step, is pretty straightforward too.

Piles of statistics and financial numbers are…well, not exactly the best material to spark fires of innovation, however important they might be for actual implementation and consensus-building.  But since a WIFIA financing is ultimately grounded in a physical project, with a specific real-world location and a clearly defined zone of impact, the piles of data can be anchored around a physical place.  The obvious way to start with data visualization in this case (the kind of presentation that does spark innovation) is a map overlay.  The graphic at the top of this post shows what some elements might look like – the base layer is the WIFIA project itself and the top layer is the WIFIA loan benefit value.  The layers in between are where the inspiration and innovation happen.

As noted in a prior post, a private-sector lender would be willing to offer a climate contingent loan for adaptation infrastructure investment if there was a way to hedge the risk that it won’t be fully repaid if extreme conditions don’t develop.  The fundamental nature of the hedge would be a parallel investment that paid more if extreme conditions don’t develop.

It’s not exactly hard to imagine entities with economic exposure to extreme climate change that would very much want some insurance for the all the costs they’ll incur in those possibly disastrous conditions.  They’d be willing to pay premiums for an insurance contract that didn’t pay anything if extreme climate conditions don’t develop – in which case the premiums are all gain for the writer of this contract.

So, a portfolio of climate insurance contracts (which will have gains if extreme conditions don’t develop) would hedge a portfolio of climate contingent loans (which will have losses in those conditions).  And vice-versa if extreme conditions do develop.

Fostering innovation in the US water sector is a core WIFIA Loan Program policy objective.  It’s right there in the name.  The Program has had a great start on the infrastructure finance part.  But a clear story about enabling innovation has yet to emerge.  It’s a tough objective.

WIFIA faces intrinsic challenges in this part of its mission.  Most importantly, ‘innovation’ and ‘investment-grade credit rating’ aren’t exactly a natural match.  Innovation almost always entails risk, and financing it involves a different kind of capital than the investment-grade loans that WIFIA offers.

Certainly, a WIFIA loan to a highly rated public water system can finance an innovative infrastructure project or even an experimental technology – if the system takes all the risk and puts its solid creditworthiness behind the loan.  No problem.  But can the Program then honestly claim to have enabled the innovation?  It’s conceivable that the low cost and other features of a WIFIA loan might have made a slight but essential difference in the project’s overall numbers, just enough to get it over the edge of infeasibility and into greenlight land.  That’d be a solid win, right?

Tax-exempt muni bond yields are hitting historic lows relative to US Treasuries. In the short term, this reflects supply and demand dynamics that may reverse once state & local governments start issuing more debt. But there are longer term factors – the prospect of higher tax rates and the ‘put’ precedent set by the Fed’s MLF last year – that may cause sub-Treasury rates to be persistent.

Federal infrastructure loan programs are well-positioned — for obvious and not-so-obvious reasons — to offer contingent loans for climate adaptation investment in public infrastructure projects.

Two things are certain.  The first is that a lot of basic public infrastructure will need to be built, replaced or rehabilitated in the near term.  The second is that these long-lived projects will be operating for decades in changing climate conditions.

These certainties create a potentially expensive uncertainty.  Decisions about the benefits and costs of climate adaptation must be made when projects are designed and built.  But a major input of that decision is now a moving target because the climate conditions in which the project will operate can’t be predicted with confidence.  Climate systems are too complex to model precisely and there’s not enough data for accurate extrapolation yet.  What’s the real chance over the next thirty or forty years of what used to be a 100-year rainfall, drought or sea-level rise event?  It’s certainly going to be different than the historical baseline, but by how much?  We’ll find out eventually.  But by then we’ll need to live with the outcomes of infrastructure decisions that are made today.  And there’s plenty of scope for expensive mistakes both in projects that are over-adapted (if conditions are closer to current baseline trends) and those that are under-adapted (if conditions are extreme).

These decisions are especially tough for public infrastructure in the US, most of which is funded at the state and local level.  It’s hard enough to get people to accept higher rates or taxes to pay for basic replacements and upgrades that have certain and immediately visible value to their own community.  Proposing even higher rates or taxes for additional adaptation investment that may (or may not) have value (at some point) in the future is a heavy lift that many local politicians won’t attempt.  Regardless of nuanced analyses or the simple prudence of erring on the side of caution, in many cases a local consensus for funding will not be practically possible.  Adaptation investment will too often be another can that gets kicked down the road.

That’s bad enough on a local level.  But the impact of widespread under-adapted basic infrastructure on an aggregate national level will be much worse if extreme conditions develop due to infrastructure’s very significant direct and indirect network effects.  A national undersupply of adaptation investment is the most likely result of cumulative local decisions.  Accepting that outcome – or simply ignoring it — becomes in effect a gigantic national bet that the investment won’t turn out to be necessary.  Are we feeling lucky?