It is Time to Admit the Purple Line Was a Mistake

The Path the Purple Line Will Take – Before and The View At Rock Creek Now

A.  Introduction

The proposed Purple Line, a 16-mile light-rail line passing in an arc across parts of suburban Maryland around Washington, DC, has become a fiasco.  The State of Maryland, under Republican Governor Larry Hogan, is preparing to sign a new contract with the private concessionaire that will pay that concessionaire $3.7 billion more than had been agreed to under the existing contract.  The total cost of that contract alone (there are significant other costs on top) will now be $9.3 billion (66% more than the $5.6 billion set in the earlier contract), and the opening will be delayed by at least a further 4 1/2 years (thus doubling the originally contracted construction period – now to a total of 9 years).  And the governor is doing this with no legislative approval being sought.

The Purple Line has long been controversial – due to its high cost, the disruption it is causing to a number of suburban neighborhoods, the destruction of parkland it has been routed through, and its use of scarce resources for public transit to benefit a privileged few rather than the broader community.  There are alternatives that would not only be far more cost-efficient but also less environmentally destructive.  The project illustrates well why the US has such poor public transit and poor public infrastructure more generally, as scarce resources are channeled into politically-driven white elephant projects such as this.

In response to the announcement of the terms of the revised contract with the concessionaire, I submitted to the Washington Post a short column for its “Local Opinions” section.  They have, however, declined to publish it.  This is not terribly surprising, as the Washington Post Editorial Board has long been a strong proponent of the Purple Line, with numerous editorials pushing strongly for it to go forward.  And while the Post claims that it supports an active debate on such issues, the guest opinion columns it has published, as well as letters-to-the-editor, have been very heavily weighted in number to those with a similar view as that of its Editorial Board.

I am therefore posting that column here.  It has been slightly edited to reflect developments since it was first drafted, but has been kept in style to that of an opinion column.

Opinion columns must also be short, with the Post setting a tight word limit.  That means important related issues can not be addressed due to the limited space.  But with room here, I can address several of them below.  Finally, I will discuss the calculations behind two of the statements made in the column, as a “fact check” backing up the assertions made.  These should themselves be of interest to those interested in the Purple Line project (and in public transit more broadly), as they illustrate factors that should be taken into account when assessing a project such as this.

B.  The Column Submitted to the Washington Post

This is the column submitted to the Post, with some minor changes to reflect developments since it was first drafted:

               It is Time to Admit the Purple Line was a Mistake

Governor Hogan has re-negotiated the contract with the private concessionaire that will build and operate the 16-mile long Purple Line through suburban Maryland.  The Board of Public Works has approved it, and despite an extra $3.7 billion that will be spent the Maryland legislature will have no vote.  The private concessionaire will now be paid $9.3 billion, a 66% increase over the $5.6 billion cost in the original contract.  And this is just for the contract with the concessionaire.  The total cost, including contracts with others (such as for design and engineering work) as well as direct costs at the Maryland Department of Transportation (MDOT), is likely well over $10 billion.

The amount to be paid to the concessionaire for the construction alone will rise to $3.4 billion from the earlier $2.0 billion, an increase of 70%.  And even though the construction is purportedly halfway complete (with $1.1 billion already spent), the remaining amount ($2.3 billion) is larger than the original total was supposed to be.  And the amount being paid to the private contractors for the construction will in fact be even higher, at $3.9 billion, once one includes the $219 million MDOT has paid directly to the subcontractors in the period since the primary construction contractor withdrew, and the $250 million paid to that primary contractor in settlement for the additional construction expenses it incurred.  That $3.9 billion is close to double the $2.0 billion provided for in the original contract.  In addition, the project under the new contract will require an extra 4 1/2 years (at least) before it is operational, doubling the time set in the original contract to 9 years.  Even though the project is purportedly halfway built, the remaining time required will equal the time that was supposed to have been required under the original plan for the entire project.

The critics were right.  They said it would cost more and take longer than what Maryland asserted (and with supposedly no risk to the state due to the “innovative” contract).  It also shows that it is silly to blame the opponents of the project.  The lawsuit delayed the start of construction by less than 9 months.  That cannot account for a delay of 4 1/2 years.  Furthermore, the state had the opportunity during those 9 months to better prepare the project, acquire the land required, and finalize the engineering and design work.  Construction should then have been able to proceed more smoothly.  It did not.  It also shows that Judge Leon was right when he ruled that the project had not met the legal requirements for being adequately prepared.

Even the state’s own assessment recognized that such a rail line was marginal at best at the costs originally forecast.  With the now far higher costs, no unbiased observer can deny that the project is a bad use of funds.  The only possible question is whether, with what has already been spent, the state should push on.  But so far only $1.1 billion has been spent on the construction, plus the state agreed to pay the former construction company the extra $250 million when it quit the project.  Thus close to $8 billion (plus what the state is spending outside of the contract with the concessionaire) would be saved by stopping now.

There are far better uses for those funds.  A top priority should be to support public transit in Montgomery and Prince George’s countries.  Even at the originally contracted cost for the Purple Line there would have been sufficient funds not only to double capacity on the county-run bus systems (doubling the routes or doubling the frequency on the routes or some combination), but also to end charging any fares on those buses.  Those bus systems also cover the entire counties, not simply a narrow 16-mile long corridor serving some of the richest zip codes in the nation.  In particular, better service could be provided to the southern half of Prince George’s, the location of some of the poorer communities in the DC area and where an end to bus fares would be of particular benefit.

Covid-19 has also now shown the foolishness of spending such sums on new fixed rail lines.  DC area Metro ridership is still 80% below where it was in 2019.   Rail lines are inflexible and cannot be moved, and in its contract the state will pay the concessionaire the same even if no riders show up.  Who knows what will happen to ridership in the 35 years of this contract?  In contrast, bus routes and frequency of service have the flexibility to be adjusted based on whatever develops.

It is time to cut our losses.  Acknowledge it was a mistake, don’t sign the revised contract, and use the funds saved to provide decent public transit services to all of our residents.

C.  Additional issues

a)  The Cost of Not Keeping the Original Construction Contractor

Media coverage of the proposed new contract has focussed on the overall $9.3 billion cost (understandably), as well as the cost of the construction portion alone.  The figure used for that construction cost has been $3.4 billion, a 70% increase over the originally contracted $2.0 billion cost.

But as noted in the column I drafted above, that $3.4 billion excludes what MDOT has paid directly to the subcontractors who have continued to work on the project since September 2020 (under the direct supervision of MDOT) after the original primary contractor (Fluor, a global corporation with projects on six continents) exited.  According to a report by MDOT in January 2022, $219 million was paid directly by MDOT for this work, and this will be in addition to the $3.4 billion to be paid to the concessionaire.  One should also add in the $250 million Maryland has agreed to pay the original primary contractor in the settlement for its claims that it incurred an additional $800 million in construction expenses on the project – expenses that were the fault of the state from an inadequately prepared project.  That $250 million was for construction costs incurred, and should be included as part of the overall construction costs that MDOT is paying the concessionaire.  The total to be paid for the construction (if there are no further cost increases, which based on the experience so far cannot be guaranteed) is thus in fact $3.9 billion.  This is close to double the original contracted cost of $2.0 billion.

This also raises another issue, which remarkably does not appear to have been discussed (from all that I have read).  The original contractor in 2020 had requested an additional $800 million in compensation for extra costs incurred in the project that it argued were the fault of the state.  One can debate whether this was warranted and whether it was the fault of the state or the contractor, but the amount claimed was $800 million.  Thus, had the state agreed, the total cost would then have been $2.8 billion, up from the originally contracted $2.0 billion.  The state rejected this, however, and then congratulated itself for bargaining the $800 million down to “only” $250 million.

But now we see that the overall amount to be paid the private firms building the rail line will be $3.9 billion.  Fluor was evidently right (even conservative) in its claim that building the project will cost more.  But the $3.9 billion it will now cost is $1.1 billion more than the $2.8 billion they would have paid had the state agreed to cover the $800 million (which probably could have been bargained down some as well).  This hardly looks like smart negotiating by Governor Hogan and his state officials.

Put another way, state officials refused to pay an extra $800 million for the project, insisting that that cost was too high.  They then negotiated a contract where instead of paying $800 million more they will pay $1.9 billion more – for the same work.  And then they sought praise for negotiating a new agreement where they will pay “only” an extra $1.9 billion.

Furthermore, the re-negotiated contract will not only pay $1.9 billion more for the construction, but also higher amounts for the subsequent 30 years when the concessionaire will operate and maintain the line.  Maryland had agreed to pay a total of $2.3 billion for this over the 30 years in the original 2016 contract, but in the re-negotiated contract will now pay $2.6 billion, an increase of $300 million.  Governor Hogan had earlier asserted that under its “innovative” PPP contract, the state would not have to cover any cost increases for the rail line operations over those 30 years – but now it does.  In addition, due to the now far higher construction costs and the proportionately much higher share of those costs that will be funded by borrowing (as the up-front grants to be provided will be largely the same – $1.36 billion will now be provided, vs. $1.25 billion before), the total financing costs over the life of the contract will now be $2.8 billion versus $1.3 billion before, an increase of $1.5 billion.  Thus the total contract will now cost $9.3 billion versus $5.6 billion before, an increase of $3.7 billion (which equals the $1.9 billion on construction + $0.3 billion on operations + $1.5 billion on financing).

It is difficult to see how there is any way this can be interpreted as smart negotiating.

b)  Don’t Blame the Lawsuit for the Problems

The politicians responsible for the Purple Line, starting with Governor Hogan, blame the lawsuit brought by opponents of the Purple Line for all the problems that followed.  This is simply wrong, and indeed silly.  The ruling by Judge Richard Leon delayed the start of construction by less than 9 months.  This cannot account for a delay that will now be at least 4 1/2 years (assuming no further delays).  Nor can it account for a project cost that is now $3.7 billion higher.

Judge Leon ruled in August 2016 that the State of Maryland had not fulfilled the legal conditions required for a properly prepared project.  The primary issue was whether a project such as this, with the unavoidable harm to the environment that a new rail line will have, is necessary to provide the transit services needed in the corridor.  Could there be other options that would provide the services desired with less harm to the environment?  If so, the law requires that they be considered.  The answer depends critically on the level of ridership that should be expected, and the State of Maryland argued that only a rail line would be able to handle the high ridership load they forecast.  Many of the Purple Line riders would be transferring from and to the DC Metrorail lines it would intersect, and the State of Maryland claimed that the DC Metrorail system (just Metro, for short) would see a steady rise in ridership over the years and thus serve as a primary draw for Purple Line riders.

Judge Leon observed that in fact Metro ridership had been declining in the years leading up this case (2016), and ruled that Maryland should look at this issue and determine whether, based on what was then known, a less environmentally destructive alternative to the Purple Line might in fact be possible.  If Maryland had complied with this ruling, they could have undertaken such a study and completed it within just a few months.  There would have been little surprise if such a study, under their own control, would have concluded that the Purple Line was still warranted.  The judge would have accepted this, and they could then have proceeded, with little to no delay.  Construction had only been scheduled to begin in October 2016.

Instead, the State filed numerous motions to reverse the ruling and to be allowed to proceed with no examination of their ridership assumptions.  They argued in those motions that there would be a steady rise in Metro ridership over time, and that by the year the Purple Line would open (then expected to be in 2022) Metro ridership would have been growing at a steady pace for years, which would then continue thereafter.  When Judge Leon declined to reverse his ruling, the State appealed and then won at the Appeals Court level.  The judges in the Appeals Court decided that the judicial branch should defer to the executive branch on this issue.  Construction then began in August 2017.  The Purple Line contractors said that they were delayed by 266 days ( = 8.7 months) as a result of Judge Leon’s ruling.

We now know that Judge Leon was in fact right in raising this concern with the prospects for Metro ridership.  Ridership on the system had in fact been falling for a number of years leading up to 2016, and it has continued to fall since then.  Metro ridership peaked in 2008, fell more or less steadily through 2016, and then continued to fall.  Ridership in 2016 was 14% below where it had reached in 2008 (despite the Silver Line opening with four new stations in 2015), and then was even less than 2016 levels in 2019.  And all this was pre-Covid.  Metrorail ridership then completely collapsed with the onset of Covid, with ridership in 2020 at 72% below where it was in 2019 and in 2021 at 79% below where it was in 2019.

Judge Leon was right.  Even setting aside the collapse in ridership with the onset of Covid, Metro ridership declined significantly and more or less steadily for more than a decade.  It was not safe to assume (as the state insisted in its court filings would be safe to assume) that Metro ridership would resume its pre-2008 upward climb.  And now we have seen not only the collapse in Metro ridership following from Covid, but also the near certainty that it will never fully recover due to the work-from-home arrangements that became common during the Covid crisis and are now expected to continue at some level.

In addition and importantly, while the Purple Line contractor noted that the judicial ruling delayed the start of construction by 266 days, this does not mean project completion should have been delayed by as much.  As Maryland state officials themselves noted, while the ruling meant construction could not start, the state could (and did) continue with necessary preparatory work, including final design work, acquisition of land parcels that would be needed along the right of way, and the securing of the necessary clearances and permits that are required for any construction project.  The state was responsible for each of these.  With the extra 9 months they should have been able to make good progress on each, and with this then ensure that the project could proceed smoothly and indeed at a faster pace once they began.

This turned out not to be the case.  Despite the extra 9 months to prepare, the Purple Line contractors cited each of these as major problems causing delays and higher costs.  Final designs were not ready on time or there had to be redesigns (as for a crash wall that has to be built for the portion of the Purple Line that will run parallel to CSX train tracks); state permits were delayed and/or required significant new expenditures (such as for the handling of water run-off); and the state was late in acquiring “nearly every” right of way land parcel required (there were more than 600) – and “by more than two years in some cases”.

An extra 9 months for preparation should have led to fewer such issues.  That they still were there, despite the extra 9 months, makes one wonder what the conditions would have been had they started construction 9 months earlier.  The extra time to prepare the project – where these were later revealed still to be major problems – likely saved the project money compared to what would have been the case had they started construction earlier.  It simply makes no sense now to blame that extra 9 months for the difficulties when they in fact had an extra 9 months to work on them.

c)  Diversion of MARC Revenues to Get Around Maryland’s Public Debt Limits

Under the Purple Line contract, the State of Maryland will be obliged to pay the concessionaire certain set amounts over 35 years, starting with a payment of $100 million when operations start (in a planned 4 1/2 years from now), but especially then for the following 30 years when the concessionaire will operate the line.  The state will be obliged to make those payments for those 30 years on the sole condition that the rail line is available to be operated (i.e. is in working order).  Hence those payments are called “availability payments”.  The payments will be the same regardless of ridership levels.  Indeed, they will have to be made (and in the same amount) even if no riders show up.  A major share of the availability payments will be made up of what will be required to cover the principal and interest on the loans that the concessionaire will be taking out to finance the construction of the project, with the repayment then by the state through the availability payments.  The concessionaire is in essence borrowing on behalf of the state, and the loans will then be repaid by the state via the concessionaire.

These long-term budget obligations are similar to the obligations incurred when the state borrows funds via a bond being issued.  Indeed, this can hardly be disputed for the borrowing being done by the concessionaire to finance the construction, with the state then repaying this through the availability payments.  it is also, at 35 years, a longer-term financial obligation than any bond Maryland has ever issued.  Governor Hogan will be tying the hands of future governors for a very long time, as failure to repay on the terms he negotiated would be an event of default.

Due to concerns of excessive government borrowing undermining finances, many states have set limits on the amount they can borrow.  In Maryland, the state has set two “capital debt affordability ratios”, which limit outstanding, tax-supported, state debt to less than 4% of Maryland personal income and the debt service that will be due on this debt to less than 8% of state tax and other revenues.

If the 35-year long Purple Line obligations were treated as state debt, then there could be a problem of Maryland running close to, and possibly exceeding, these debt affordability ratios.  This is discussed in further detail in an annex at the end of this blog post, with illustrative calculations.  Exceeding those limits would be a significant issue for the state, and might conceivably put it in violation of conditions written into the contracts for its outstanding state bonds.  To avoid this, or even if the Purple Line obligations would bring it closer to but not over those limits, Maryland would need to limit its public sector borrowing, postponing other projects and programs due to the limited borrowing space that the Purple Line has used up.

The issue is not new.  It already arose in the contract signed in 2016.  But it will be even more important now due to the higher cost of the concession  – $9.3 billion to be paid to the concessionaire vs. $5.6 billion before.

Lawyers can debate whether the payment obligations (or a portion of them, e.g. the portion directly tied to the debt incurred by the concessionaire on behalf of the state) should or should not be included in the state’s capital debt affordability ratios.  But to forestall such a debate, MDOT has chosen to create a special trust account from which all payments for the Purple Line would be made.  That trust would be funded by Purple Line fare revenues (whatever they are) and grant funds received for the project (primarily from federal sources).  But MDOT acknowledges that such funding would not suffice for the financial obligations being incurred for the Purple Line, at least for some time.  And if direct support to cover this was then provided from the Maryland state budget, where revenues come primarily from taxes, the Purple Line obligations would be seen as tax-supported debt and hence subject to the borrowing limits set by the capital debt affordability ratios.

So instead of openly providing funding directly from the state budget, they will channel fare revenues collected on MARC (the state-owned commuter rail system) in the amounts necessary to cover the payment obligations on the Purple Line.  But MARC does not run a surplus.  Like other commuter rail lines it runs a deficit.  Each dollar in MARC fares channeled to cover Purple Line payment obligations thus will increase that MARC deficit by a dollar.  But then, for reasons that make little sense to an economist but which a lawyer might appreciate, those higher MARC deficits can be covered by increased funding from the state budget without this impacting the state’s capital affordability limits.  The identical payments if sent directly to cover the Purple Line obligations, however, would be counted against those ratios.

But this is just a shell game.  The funding to cover the Purple Line payment obligations are ultimately coming from the state budget, and routing it via MARC transfers simply serves to allow the state to bypass the capital debt affordability limits.  It also reduces transparency on how the Purple Line costs are being covered.

Nor are the agencies that assign ratings to Maryland state bonds being fooled by this.  S&P, for example, noted specifically that it will take into account the payment obligations on the Purple Line when they compute for themselves what the capital debt affordability ratios in fact are.

d)  Role (or Lack of It) of the State Legislature

Under the new contract Governor Hogan and his administration have negotiated, a total of $9.3 billion will be paid to the concessionaire, or $3.7 billion more than the $5.6 billion that was to be paid under the original contract.  The state legislature will apparently have no say in this.  While it will bind future administrations to make specified payments over a 35 year period, with payments that must be made regardless of ridership or any factor the state has control over (the rail line needs merely to be “available”), the only recognized check on this is apparently a vote in the Board of Public Works.  But there are only three members on this Board, only two votes are required for approval, and the governor has one of those two votes.  The legislature has no role.

I find this astonishing.  The state legislature is supposed to set the budget, but no vote will be taken on whether the further $3.7 billion should be spent.  Indeed, it appears the legislature would have no role regardless of how much the current governor is binding his successors to pay (Governor Hogan will be long out of office when the payments are due), nor for how long.  Suppose it was twice as much, or ten times as much, or whatever.  And while this commitment will be for 35 years to 2056 (five years past what was in the original contract), it appears the same would apply if the revised contract were extended to 50 years, or 100 years, or whatever.  Under the current rules, it appears that the legislature has accepted that the governor can commit future administrations to pay whatever he decides and for as long as he decides, with just the approval of the Board of Public Works.

This is apparently a consequence of the state law passed in 2013 establishing the process to be followed for state projects that would be pursued via a Public-Private Partnership (PPP) approach.  The Purple Line is the first state project being pursued on the basis of that 2013 legislation, with the legislature approving also in 2013 the start of the process on the Purple Line.  This legislative approval was provided on the basis of cost estimates provided to it at the time.  MDOT then issued a Request for Qualifications in November 2013 to identify interested bidders, a Request for Proposals in July 2014, and received proposals from four bidders in November and December 2015.  Following review and final negotiations, MDOT announced the winning bidder on March 1, 2016.  Only then did they know what the cost (under that winning bid) would be, and the state legislature was given 30 days to review the draft contract (of close to 900 pages) during which time they could vote not to approve.  But no vote taken would be deemed approval.  Then, with just the approval of the Board of Public Works as well (received in early April 2016), MDOT could sign the contracts on behalf of Maryland.

However, there will be no such review by the legislature of any amendments to that contract.  Amendments apparently require nothing more than the approval of the Board of Public Works, and with that sole approval, the governor is apparently empowered to commit future administrations to pay whatever amount he deems appropriate, for as many years as he deems appropriate.  The increase in the future payment obligations in this case will be $3.7 billion, but apparently it could be any amount whatsoever, with just the approval of the Board of Public Works.

Based on this experience, one would think that the legislature would at a minimum hold public hearings to examine what went wrong with the Purple Line, and what needs to be done to ensure the legislature retains control of the state budget.  The current legislation apparently gives the governor close to a blank check (requiring only the approval of the Board of Public Works) to obligate future administrations to pay whatever amount he sees fit, for as many years as he sees fit.

Central also to any legislative review of a proposed expenditure is whether that expenditure is warranted as a good use of scarce public resources.  One can debate whether the Purple Line was warranted at the initial cost estimates.  As will be discussed below, at those initially forecast costs even the state’s own analysis indicated it was at best marginal (and inferior to alternatives).  But even if warranted at the then forecast costs, it does not mean the project makes sense at any cost.  Based on what we now know will be a far higher cost, no unbiased person can claim that the Purple Line is still (if it ever was ) a good use of public resources.

Yet remarkably, it does not appear that any assessment was done by any office in Maryland government of whether this project is justified at the now much higher costs.  The issue simply did not enter into the discussion – at least in any discussion that has been made public.  Rather, at the Board of Public Works meeting on the project, Governor Hogan praised MDOT staff for continuing to push the project forward despite the problems.  Indeed, the higher the increase in cost for the project, the more difficult it would be to proceed, and hence the more the staff should be commended (in that view) for nevertheless succeeding in pushing the project through.  This is perverse.

Legislative review is supposed to look at such issues and to set overall budget priorities.  Yet under the PPP law passed in 2013, the legislature apparently has no role to review and consider whether an amended expenditure on such a project is a good use of the budget resources available.

D.  Fact Checks

a)  The Lack of Economic Justification for the Purple Line

The column includes the statement:

Even the state’s own assessment recognized that such a rail line was marginal at best at the costs then envisaged.  With the now far higher costs, no unbiased observer can deny that the project is far from justified.

This statement is based on the results of the state’s analysis reported in the Alternatives Analysis / Draft Environmental Impact Statement, released in September 2008.  The Alternatives Analysis looked at seven options to provide improved public transit services in the Purple Line corridor – an upgrading of existing bus services (labeled TSM for Transportation System Management), three bus rapid transit options (low medium, and high), and three light rail options (low, medium, and high).  All would provide improved public transit services in the corridor.  The question is which one would be best.

The summary results from the analysis are provided in Chapter 6, and the primary measure of whether the investment would be worthwhile is the “FTA cost-effectiveness measure” – see tables 6-2 and 6-3.  The Federal Transit Administration (FTA) cost-effectiveness measure is calculated as the ratio of the extra costs of the given option (extra relative to what the costs would be under the TSM option, and with both annualized capital costs and annual operational and maintenance costs), to the extra annual hours of user benefits of that option relative to the TSM option.  That is, it is a ratio of two differences – the difference in costs (relative to TSM) as a ratio to the difference in benefits (again relative to TSM).  Thus it is a ratio of costs to benefits, and a higher number is worse.  Hours of user benefits are an estimate of the number of hours saved by riders if the given transit option is available, where they mark up those hours saved by a notional factor to account for what they say would be a more pleasant ride on a light rail line (which biases the results in favor of a rail line but, as we will see, not by enough even with this).

The FTA issues guidelines classifying projects by their cost-effectiveness ratios.  For FY2008 (the relevant year for the September 2008 Alternatives Analysis), the breakpoints for those costs were (see Table II-2 in Appendix B of the FTA’s FY2008 Annual Report on Funding Recommendations):

High (meaning best) $11.49 and under
Medium-High $11.50 – $14.99
Medium $15.00 – $22.99
Medium-Low $23.00 – $28.99
Low (meaning worst) $29.00 and over

The Alternatives Analysis estimated that the Medium Light Rail Line option would have a cost-effectiveness ratio of $22.82.  This would place it in the Medium category for the FTA cost-effectiveness measures, but just barely.  This was important, as the FTA will very rarely consider for federal grant funding a project in its Medium-Low category, and never in the Low category.

The other two light rail options examined had worse cost-effectiveness ratios ($26.51 and $23.71 for the Low and High options respectively) that would have placed them in FTA’s Medium-Low cost-effectiveness category, and thus highly unlikely to be accepted by the FTA for funding.  Not surprisingly, the Governor of Maryland (O’Malley at the time) selected the Medium Light Rail option as the state’s preferred option, as the other two light rail options would likely have been immediately rejected, while the Medium Light Rail choice would have been within the acceptable limits – although just barely so.  And while in principle they chose the Medium Light Rail option, they then added features (and costs) to it that brought it closer to what had been the High Light Rail Option, while not re-doing the cost-effectiveness analysis.

Maryland should also have considered any of the three Bus Rapid Transit options, as their cost-effectiveness measures were uniformly better than any of the light rail options (with cost-effectiveness ratios of $18.24, $14.01, and $19.34 for the Low, Medium, and High options respectively).  They were better even without the scaling-up of user benefits (by a notional factor for what was claimed would be a more pleasant ride) that biased the results in favor of the light rail options.  And most cost-effective of all would have been a simple upgrading of regular bus services, introducing express lines and other such services where there is a demand.

These were all calculated at the costs as estimated in 2008.  We now know that the costs for the light rail line option chosen will be far higher than what was estimated in 2008.  That cost then was estimated to be $1.2 billion to build the line, and an annual $25.0 million then for operations and maintenance.  Adjusting these figures for general inflation from the prices of 2007 (the prices used for these estimates) to those of December 2021 would raise them by 34%, or to $1.6 billion for the capital cost and $33.5 million for the annual operational and maintenance costs.  But under the new contract, the capital cost will be $3.9 billion, or 2.4 times higher than estimated in 2008 (in end-2021 prices).  Also, the annual operational and maintenance costs (including insurance) in the new contract will be $2.6 billion over 30 years.  This payment will be adjusted for inflation, and the $2.6 billion reflects what it would be at an assumed inflation rate of 2% a year.  One can calculate that at such a 2% inflation rate, the annual payment over the 30 years in the prices of end-2021 would be $58.0 million, or 73% higher than the $33.5 million had been forecast earlier (also at end-2021 prices).

Putting the capital cost in annualized terms in the same way as was done in the Alternatives Analysis report, and adding in the annual operational and maintenance costs, the overall costs under the new contract (with all in end-2021 prices) is 2.3 times higher than what was forecast in 2008, when the Medium Light Rail option was chosen.  To be conservative, I will round this down to just double.  To calculate what the FTA cost-effectiveness measures would have been (had the forecast costs been closer to what the new contract calls for), one also needs ridership forecasts.  While we know that those forecasts are also highly problematic (as discussed in this earlier blog post, they have mathematical impossibilities), for the purposes here I will leave them as they were forecast in the Alternatives Analysis.

Based on this, one can calculate that the FTA cost-effectiveness measure would have jumped to $50.55 had the capital and operating costs been estimated closer to what they now are under the new contract.  This would have put the Purple Line far into the Low category for cost-effectiveness (far above the $29.00 limit), and the FTA would never have approved it for funding.  And at more plausible ridership estimates, the ratio would have been higher still.

b)  For the Cost of the Purple Line, One Could Double Bus Services in Suburban Maryland, and Stop Charging Fares

Resources available for public transit are scarce, and by spending them on the Purple Line they will not be available for other transit uses.  The Purple Line will serve a relatively narrow population – those living along a 16-mile corridor passing through some of the richest zip codes in the country, providing high-end services to a relatively few riders.  The question that should have been examined (but never was) was whether the resources being spent on the Purple Line could have been used in a way that would better serve the broader community.

A specific alternative that should have been considered would have been to use the funds that are being spent on the Purple Line instead to support public transit more broadly in Montgomery and Prince George’s Counties.  What could have been done?  The alternatives can then be compared, and a determination made of which would lead to a greater benefit for the community.  Only with such a comparison can one say whether a proposed project is worthwhile.

Specifically, what could be done if such resources were used instead to support the local, county-run bus services in Montgomery and Prince George’s Counties (Ride-On and The Bus respectively)?  They already carry twice as many riders as what the Purple Line would have carried in the base period examined (according to its optimistic forecasts), had it been in operation then.  As we will see below, with the funds that the State of Maryland will make in the availability payments on the Purple Line (and net of forecast Purple Line fare revenues), one could instead end the collection of all fares on those bus systems and at the same time double the size of those systems (doubling the routes or doubling the frequency on the current routes, or, and most likely, some combination of the two).  With unchanged average bus occupancy, they could thus serve four times the number of riders that the Purple Line is forecast (optimistically, but unrealistically) to carry.

The services would also be provided to the entire counties, not just to those living along the Purple Line’s 16-mile corridor.  Especially important would be service to the southern half of Prince George’s County, where much of its poorer population lives.  The Purple Line will not be anywhere close to this.  Ending the collection of fares would also be of particular value to these riders.

For the comparison to the cost of running the county-run bus systems, I used data on their operating costs, capital costs, and fare revenues from the National Transit Database, which is managed by the Federal Transit Administration of the US Department of Transportation.  The data was downloaded on February 1, 2022.  The data is available through 2020, but I used 2019 figures so as not to be affected by the special circumstances of the Covid-19 pandemic.

The bus system costs in 2019, along with what the Purple Line costs will be, are:

(in millions of $)

County-Run Bus Systems (for 2019):
Operating costs $157.6
10-year average K costs $17.1
  Total costs $174.7
Fares collected $22.0
  Total to double capacity and no fares $196.7
Purple Line:
Annual availability payments $240.0
Less fares collected (forecast) $45.3
  Net Costs $194.7

The two bottom-line figures basically match, at around $195 million.  The net payments that will be made on the Purple Line over its 30-year life would be $194.7 million, based on the announced availability payment averaging $240.0 million per year less forecast average annual fares to be collected.  That average fare forecast is undoubtedly optimistic (as the ridership forecasts are optimistic), and is based on what was provided in 2016 when the original contract was discussed with the legislature.  I have not seen an updated forecast, but MDOT staff stated (at the Board of Public Works meeting on January 26 to discuss and vote on the new contract) that fares would not be changed from what was planned before.

The cost of doubling the size of the county-run bus systems would have been $157.6 million for the operating cost (based on the actual cost in 2019) plus $17.1 million for the capital cost (based on the 10-year annual average between 2010 and 2019, as these expenditures fluctuate a good deal year to year), or a total of $174.7 million.  It is assumed that government will continue to spend what it is spending now to support these bus systems, so the extra funding needed for doubling the systems would be those costs again (for that second half), plus what is received in fare revenues in the system now (the $22.0 million) as fares would no longer be collected.  Thus the net cost would be $196.7 million, very close to the amount that could be covered by what will be provided on a net basis to the Purple Line (and assuming, optimistically, fares averaging $45.3 million a year).

In addition to this, a total of $1.36 billion will be provided in grants to the Purple Line.  At the lower cost of the earlier, 2016, contract, a portion of those grant funds ($1.25 billion before) would have been needed to cover a share of the costs of doubling the capacity of the bus systems and ending the collection of fares.  One could in principle have invested those grant funds and at a reasonable interest rate have generated sufficient funds to close the remaining gap.  But with the now far higher costs of the renegotiated contract, there would be no need for a share of those grant funds for this, and they could instead be used to provide funding for other high-priority transit needs in the region.

E.  Conclusion

The Purple Line has long been a problematic project, and with the now far higher costs in the renegotiated contract with the concessionaire, can only be described as a fiasco.  After rejecting a demand from the contractor to pay $800 million more to complete the construction of the rail line, they will instead now pay $1.9 billion more to a total of $3.9 billion for the construction alone, or close to double the originally negotiated cost of $2.0 billion.  They will also now pay more for the subsequent operation of the line.  It is all a terribly wasteful use of the scarce funds available for public transit, and comes with great environmental harm on top.  Funds that will be spent by the state under this concession contract could have been far better used, and far more equitably used, by supporting the public transit systems that serve the entire counties.

Despite the much higher costs, there does not appear to have been any serious assessment of whether the Purple Line can be justified at these higher costs.  At least there has not been any public discussion of this.  Rather, MDOT staff appear to have been directed to do whatever it takes, and at whatever the cost it turns out to be, to push through the project.  But that is in fundamental contradiction to basic public policy.  A project might be warranted at some low cost, but that does not then mean it is still warranted if it turns out the cost will be far higher.  That needs to be examined, but there is no evidence that there was any such examination here.

We should also now recognize as obvious that forecasts of ridership on fixed rail lines are uncertain.  Ridership on the DC Metro rail lines not only fell, more or less steadily, over the decade leading up to 2019, but then collapsed in 2020 and 2021 due to the Covid crisis.  Ridership in 2021 was almost 80% below what it was in 2019.  And it is highly unlikely that Metrorail ridership will ever recover to its earlier levels, as many of the former commuters on the system will now be working from home for at least part of the workweek.

Despite this, Governor Hogan has adamantly refused to look at alternatives to building a new fixed rail line, with this to be paid for via a 35-year long concession with private investors that will tie his successors to making regular availability payments regardless of whatever ridership turns out to be, and regardless of any other developments that might lead to more urgent priorities for the state’s budget resources.  The issue is not only that the ridership forecasts on the Purple Line are highly problematic, with mathematical impossibilities and other issues.  It is also, and more importantly, that any such ridership forecasts are uncertain.  Just look at what happened with Covid.  It was totally unanticipated but led ridership to collapse almost literally overnight.  And the effects are still with us, almost two years later.

The fundamental failure is the failure to acknowledge that any such forecasts are uncertain, and highly so.  There might be future Covids, and also other future events that we have no ability to foresee or predict.  For precisely this reason, it is important to design systems that are flexible.  A rail line is not.  Once it is built (at great cost), it cannot be moved.  Bus routes, in contrast can be shifted when this might be warranted, as can the frequency of services on the routes.

None of this seems to have mattered in the decisions now being taken.  As a consequence, and despite billions of dollars being spent, we do not have the transit systems that provide the services our residents need.

 

 

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Annex:  Details on the Diversion of MARC Revenues to Get Around Maryland’s Public Debt Limits

The State of Maryland follows a policy to limit its public borrowing so that state debt does not become excessive.  Specifically, it has set two “capital debt affordability ratios”:

1) Keep the stock of tax-supported state debt below 4% of personal income in the state;

and 2)  Keep debt service on tax-supported state debt below 8% of state revenues.

I am not sure whether these are limits have been set by statute, but as policy they will in any case be reflected in the state bond ratings.  It is also possible that representations, and perhaps even covenants, have been made in the Maryland state bond contracts stating the intention of the state to keep to them.  If so, then violation of those limits could have consequences for those bonds, possibly putting the state technically in default.

The commitments Governor Hogan will be making in signing the concession contracts for the Purple Line are in essence the same as commitments made when the state issues a bond and agrees to pay amortization and interest on that bond as those payments come due.  For the Purple Line, the private concessionaire will similarly be borrowing funds, but the State of Maryland will then have the obligation under the contract to repay that borrowing through the availability payments to be paid to the concessionaire for 30 years.  In addition to repaying (with interest) the borrowings made by the concessionaire, the availability payments will also cover the operational, maintenance, and similar costs over the 30-year life of the contract during which the concessionaire will operate the line.

Under the original contract, signed in 2016, these payments were expected to average $154 million per year for 30 years.  Under the new contract, they are expected to average $240 million a year.  One can debate whether all of the availability payment (which includes payment for the operations and maintenance) or simply some share of these payments should be considered similar to debt, but the payment obligation is fundamentally the same.  Governor Hogan is committing future governors (up until 2056) to make these payments, with the sole condition that the concessionaire has ensured the rail line is available to be used (hence the label “availability payments”).  In particular, they will be obliged to make these payments regardless of what ridership turns out to be, or indeed whether any riders show up at all.  That risk is being taken on fully by the state and is not a concern of the concessionaire (who, indeed, will find things easier and hence preferable the fewer the number of riders who show up).

These availability payments have all the characteristics of a debt obligation.  But if it were treated as state debt, it would have to be included in the capital debt affordability limits, and this could affect the amount that the state could borrow for other purposes.  One can debate precisely what obligations to include and the timing of when they should be included, but purely for the sake of illustration, let’s use the 2016 contract amounts and assume that the obligation to be repaid would have had a capital value of $2.0 billion (equal to the then planned construction cost, minus grants received for it, but plus the present discounted value of non-debt operating and other costs that have been obligated).  Assume also this would have applied in 2017.  Based on figures in the November 2021 report of Maryland’s Capital Debt Affordability Committee (see Table 1 on page 26), the ratio of tax-supported state debt to Maryland personal income was 3.5% in 2017, or below the 4% limit.  However, if the full $2.0 billion from the Purple Line would have been added in 2017, following the contract signing in 2016, that ratio would have grown to 4.1%.

Similarly, the Capital Debt Affordability Committee report indicates (Table 2A on page 28) that debt service on tax-supported public debt in 2017 was 7.5% of state revenues.  If one were to add the full annual $154 million payment that would be due (under the original contract) for the Purple Line already in 2017 (too early, as it would not be due until construction is over, but this is just for illustration), the debt service ratio to state revenues would have risen from 7.5% without the Purple Line commitments to 8.2% with it – above the 8.0% limit.  Of the $154 million, about two-thirds would have been used to repay the funds borrowed to pay for the construction (plus for the equity, which was a small share of the total).  If one argued that only these payments on the debt incurred (and the similar equity cost) should be included, and not also the 30-year commitment to cover the operational and similar other costs, then the ratio would have risen to 7.98% if it applied in 2017 – basically at the 8.0% limit.

Again, these figures are simply for illustration, and the actual additions in 2017 would have been less and/or applied only in later years.  But as a rough indication, they indicate that the Purple Line debt and payments due would be materially significant and hence problematic.

it was thus important that MDOT structure these payment obligations in such a way that it could argue that they are not for “tax-supported public debt”.  This would be the case, for example, if the fare revenues from ridership on the Purple Line would suffice to cover the debt service and other payment obligations incurred.  But even MDOT had to concede the Purple Line revenues would not suffice for that in at least the early years, although it did assert (unconvincingly) that ultimately they would.

MDOT therefore established a separately managed trust for the Purple Line, which would be used to make the payments due and into which it would direct not simply Purple Line fare revenues and grants to be received for the project (primarily from federal sources), but also sufficient revenues from the MARC commuter rail line (operated by MDOT) to make the payments.  It argued also that only the debt service component of the availability payment would have to be included (about two-thirds of the total payment obligation in the 2016 contract), with the operations, maintenance, and other such costs not relevant to the capital debt affordability ratios (despite being a long-term, 30-year, commitment).  The State Treasurer, Nancy Kopp in 2016, ruled that this structure was acceptable and that Purple Line debt should thus not count against the state’s capital debt affordability limits.

But while deemed not applicable for the capital debt affordability limits, the immediate question that arises is what then happens to MARC?  Commuter rail lines in the US do not run a surplus, and require subsidies from a government budget to remain in operation.  MARC is no exception.  If a portion of MARC revenues are diverted to cover payments on Purple Line debt, then MARC’s deficit will rise by that amount and Maryland’s subsidies to MARC will have to rise by that same amount.  And those subsidies will come from state tax revenues.  Hence state tax revenues are in reality covering the Purple Line debt payments, and routing it via MARC does not change that reality.  At a minimum, transparency is being lost.

Furthermore, and as noted before, the state bond rating agencies have made it known that they are fully aware of what is going on, and will include these Purple Line obligations into their calculations.  S&P explained in May 2016 that upon the signing of the Purple Line contract, they will include the net present value of the payments to be made by the state during the construction period in their calculations of the state’s tax-supported debt ratios, and that once operations begin will include in the ratios the full availability payments net of fare revenues collected on the Purple Line only.

Maryland’s payment commitments under the revised Purple Line contract are now expected to average $240 million a year, far above the $154 million expected before.  MDOT has once again made its case with the new State Treasurer (Dereck Davis, who took office on December 17, 2021, replacing the long-time former Treasurer Kopp) that these long-term payment obligations should not count against the state’s Capital Debt Affordability Ratios.  While I have not seen a formal ruling on this from the State Treasurer’s office, presumably he agreed with the MDOT view as otherwise it would not have been presented to the Board of Public Works on January 26.

The Ridership Forecasts for the Baltimore-Washington SCMAGLEV Are Far Too High

The United States desperately needs better public transit.  While the lockdowns made necessary by the spread of the virus that causes Covid-19 led to sharp declines in transit use in 2020, with (so far) only a partial recovery, there will remain a need for transit to provide decent basic service in our metropolitan regions.  Lower-income workers are especially dependent on public transit, and many of them are, as we now see, the “essential workers” that society needs to function.  The Washington-Baltimore region is no exception.

Yet rather than focus on the basic nuts and bolts of ensuring quality services on our subways, buses, and trains, the State of Maryland is once again enamored with using the scarce resources available for public transit to build rail lines through our public parkland in order to serve a small elite.  The Purple Line light rail line was such a case.  Its dual rail lines will serve a narrow 16-mile corridor, passing through some of the richest zip codes in the nation, but destroying precious urban parkland.  As was discussed in an earlier post on this blog, with what will be spent on the Purple Line one could instead stop charging fares on the county-run bus services in the entirety of the two counties the Purple Line will pass through (Montgomery and Prince George’s), and at the same time double those bus services (i.e. double the lines, or double the service frequency, or some combination).

The administration of Governor Hogan of Maryland nonetheless pushed the Purple Line through, although construction has now been halted for close to a year due to cost overruns leading the primary construction contractor to withdraw.  Hogan’s administration is now promoting the building of a superconducting, magnetically-levitating, train (SCMAGLEV) between downtown Baltimore and downtown Washington, DC, with a stop at BWI Airport.  Over $35 million has already been spent, with a massive Draft Environmental Impact Statement (DEIS) produced.  As required by federal law, the DEIS has been made available for public comment, with comments due by May 24.

It is inevitable that such a project will lead to major, and permanent, environmental damage.  The SCMAGLEV would travel partially in tunnels underground, but also on elevated pylons parallel to the Baltimore-Washington Parkway (administered by the National Park Service).  The photos at the top of this post show what it would look like at one section of the parkway.  The question that needs to be addressed is whether any benefits will outweigh the costs (both environmental and other costs), and ridership is central to this.  If ridership is likely to be well less than that forecast, the whole case for the project collapses.  It will not cover its operating and maintenance costs, much less pay back even a portion of what will be spent to build it (up to $17 billion according to the DEIS, but likely to be far more based on experience with similar projects).  Nor would the purported economic benefits then follow.

I have copied below comments I submitted on the DEIS forecasts.  Readers may find them of interest as this project illustrates once again that despite millions of dollars being spent, the consulting firms producing such analyses can get some very basic things wrong.  The issue I focus on for the proposed SCMAGLEV is the ridership forecasts.  The SCMAGLEV project sponsors forecast that the SCMAGLEV will carry 24.9 million riders (one-way trips) in 2045.  The SCMAGLEV will require just 15 minutes to travel between downtown Baltimore and downtown Washington (with a stop at BWI), and is expected to charge a fare of $120 (roundtrip) on average and up to $160 at peak hours.  As one can already see from the fares, at best it would serve a narrow elite.

But there is already a high-speed train providing premier-level service between Baltimore and Washington – the Acela service of Amtrak.  It takes somewhat longer – 30 minutes currently – but its fare is also somewhat lower at $104 for a roundtrip, plus it operates from more convenient stations in Baltimore and Washington.  Importantly, it operates now, and we thus have a sound basis for forecasts of what its ridership might be in the future.

One can thus compare the forecast ridership on the proposed SCMAGLEV to the forecast for Acela ridership (also in the DEIS) in a scenario of no SCMAGLEV.  One would expect the forecasts to be broadly comparable.  One could allow that perhaps it might be somewhat higher on the SCMAGLEV, but probably less than twice as high and certainly less than three times as high.  But one can calculate from figures in the DEIS that the forecast SCMAGLEV ridership in 2045 would be 133 times higher than what they forecast Acela ridership would be in that year (in a scenario of no SCMAGLEV).  For those going just between downtown Baltimore and downtown Washington (i.e. excluding BWI travelers), the forecast SCMAGLEV ridership would be 154 times higher than what it would be on the comparable Acela.  This is absurd.

And it gets worse.  For reasons that are not clear, the base year figures for Acela ridership in the Baltimore-Washington market are more than eight times higher in the DEIS than figures that Amtrak itself has produced.  It is possible that the SCMAGLEV analysts included Acela riders who have boarded north of Baltimore (such as in Philadelphia or New York) and then traveled through to DC (or from DC would pass through Baltimore to ultimate destinations further north).  But such travelers should not be included, as the relevant travelers who might take the SCMAGLEV would only be those whose trips begin in either Baltimore or in Washington and end in the other metropolitan area.  The project sponsors have made no secret that they hope eventually to build a SCMAGLEV line the full distance between Washington and New York, but that would at a minimum be in the distant future.  It is not a source of riders included in their forecasts for a Baltimore to Washington SCMAGLEV.

The Amtrak forecasts of what it expects its Acela ridership would be, by market (including between Baltimore and Washington) and under various investment scenarios, come from its recent NEC FUTURE (for Northeast Corridor Future) study, for which it produced a Final Environmental Impact Statement.  Using Amtrak’s forecasts of what its Acela ridership would be in a scenario where major investments allowed the Acela to take just 20 minutes to go between Baltimore and Washington, the SCMAGLEV ridership forecasts were 727 times as high (in 2040).  That is complete nonsense.

My comment submitted on the DEIS, copied below, goes further into these results and discusses as well how the SCMAGLEV sponsors could have gotten their forecasts so absurdly wrong.  But the lesson here is that the consultants producing such forecasts are paid by project sponsors who wish to see the project built.  Thus they have little interest in even asking the question of why they have come up with an estimate that 24.9 million would take a SCMAGLEV in 2045 (requiring 15 minutes on the train itself to go between Baltimore and DC) while ridership on the Acela in that year (in a scenario where the Acela would require 5 minutes more, i.e. 20 minutes, and there is no SCMAGLEV) would be about just 34,000.

One saw similar issues with the Purple Line.  An examination of the ridership forecasts made for it found that in about half of the transit analysis zone pairs, the predicted ridership on all forms of public transit (buses, trains, and the Purple Line as well) was less than what they forecast it would be on the Purple Line only.  This is mathematically impossible.  And the fact that half were higher and half were lower suggests that the results they obtained were basically just random.  They also forecast that close to 20,000 would travel by the Purple Line into Bethesda each day but only about 10,000 would leave (which would lead to Bethesda’s population exploding, if true).  The source of this error was clear (they mixed up two formats for the trips – what is called the production/attraction format with origin/destination), but it mattered.  They concluded that the Purple Line had to be a rail line rather than a bus service in order to handle their predicted 20,000 riders each day on the segment to Bethesda.

It may not be surprising that private promoters of such projects would overlook such issues.  They may stand to gain (i.e. from the construction contracts, or from an increase in land values next to station sites), even though society as a whole loses.  Someone else (government) is paying.  But public officials in agencies such as the Maryland Department of Transportation should be looking at what is the best way to ensure quality and affordable transit services for the general public.  Problems develop once the officials see their role as promoters of some specific project.  They then seek to come up with a rationale to justify the project, and see their role as surmounting all the hurdles encountered along the way.  They are not asking whether this is the best use of scarce public resources to address our very real transit needs.

A high-speed magnetically-levitating train (with superconducting magnets, no less), may look attractive.  But officials should not assume such a shiny new toy will address our transit issues.

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May 22, 2021

Comment Submitted on the DEIS for SCMAGLEV

The Ridership Forecasts Are Far Too High

A.  Introduction

I am opposed to the construction of the proposed SCMAGLEV project between Baltimore and Washington, DC.  A key issue for any such system is whether ridership will be high enough to compensate for the environmental damage that is inevitable with such a project.  But the ridership forecasts presented in the DEIS are hugely flawed.  They are far too high and simply do not meet basic conditions of plausibility.  At more plausible ridership levels, the case for such a project collapses.  It will not cover its operating costs, much less pay back any of the investment (of up to $17 billion according to the DEIS, but based on experience likely to be far higher).  Nor will the purported positive economic benefits then follow.  But the damage to the environment will be permanent.

Specifically, there is rail service now between Baltimore and Washington, at three levels of service (the high-speed Acela service of Amtrak, the regular Amtrak Regional service, and MARC).  Ridership on the Acela service, as it is now and with what is expected with upgrades in future years, provides a benchmark that can be used.  While it could be argued that ridership on the proposed SCMAGLEV would be higher than ridership on the Acela trains, the question is how much higher.  I will discuss below in more detail the factors to take into account in making such a comparison, but briefly, the Acela service takes 30 minutes today to go between Baltimore and Washington, while the SCMAGLEV would take 15 minutes.  But given that it also takes time to get to the station and on the train, and then to the ultimate destination at the other end, the time savings would be well less than 50%.  The fare would also be higher on the SCMAGLEV (at an average, according to the DEIS, of $120 for a round-trip ticket but up to $160 at peak hours, versus an average of $104 on the Acela).  In addition, the stations the SCMAGLEV would use for travel between downtown Baltimore and downtown Washington are less conveniently located (with poorer connections to local transit) than the Acela uses.

Thus while it could be argued that the SCMAGLEV would attract more riders than the Acela, even this is not clear.  But being generous, one could allow that it might attract somewhat more riders.  The question is how many.  And this is where it becomes completely implausible.  Based on the ridership forecasts in the DEIS, for both the SCMAGLEV and for the Acela (in a scenario where the SCMAGLEV is not built), the SCMAGLEV in 2045 would carry 133 times what ridership would be on the Acela.  Excluding the BWI ridership on both, it would be 154 times higher.  There is no way to describe this other than that it is just nonsense.  And with other, likely more accurate, forecasts of what Acela ridership would be in the future (discussed below) the ratios become higher still.

Similarly, if the SCMAGLEV will be as attractive to MARC riders as the project sponsors forecast it will be, then most of those MARC riders would now be on the modestly less attractive Acela.  But they aren’t.  The Acela is 30 minutes faster than MARC (the SCMAGLEV would be 45 minutes faster), yet 28 times as many riders choose MARC over Acela between Baltimore and Washington.  I suspect the fare difference ($16 per day on MARC, vs. $104 on the Acela) plays an important role.  The model used could have been tested by calculating a forecast with their model of what Acela ridership would be under current conditions, with this then compared this to what the actual figures are.  Evidently this was not done.  Had they, their predicted Acela ridership would likely have been a high multiple of the actual and it would have been clear that their modeling framework has problems.

Why are the forecasts off by orders of magnitude?  Unfortunately, given what has been made available in the DEIS and with the accompanying papers on ridership, one cannot say for sure.  But from what has been made available, there are indications of where the modeling approach taken had issues.  I will discuss these below.

In the rest of this comment I will first discuss the use of Acela service and its ridership (both the actual now and as projected) as a basis for comparison to the ridership forecasts made for the SCMAGLEV.  They would be basically similar services, where a modest time saving on the SCMAGLEV (15 minutes now, but only 5 minutes in the future if further investments are made in the Acela service that would cut its Baltimore to DC time to just 20 minutes) is offset by a higher fare and less convenient station locations.  I will then discuss some reasons that might explain why the SCMAGLEV ridership forecasts are so hugely out-of-line with what plausible numbers might be.

B.  A Comparison of SCMAGLEV Ridership Forecasts to Those for Acela  

The DEIS provides ridership forecasts for the SCMAGLEV for both 2030 (several years after the DEIS says it would be opened, so ridership would then be stable after an initial ramping up) and for a horizon year of 2045.  I will focus here on the 2045 forecasts, and specifically on the alternative where the destination station in Baltimore is Camden Yards.  The DEIS also has forecasts for ridership in an alternative where the SCMAGLEV line would end in the less convenient Cherry Hill neighborhood of Baltimore, which is significantly further from downtown and with poorer connections to local transit options.  The Camden Yards station is more comparable to Penn Station – Baltimore, which the Acela (and Amtrak Regional trains and one of the MARC lines) use.  Penn Station – Baltimore has better local transit connections and would be more convenient for many potential riders, but this will of course depend on the particular circumstances of the rider – where he or she will be starting from and where their particular destination will be.  It will, in particular, be more convenient for riders coming from North and Northeast of Baltimore than Camden Yards would be.  And those from South and Southwest of Baltimore would be more likely to drive directly to the DC region than try to reach Camden Yards, or they would alight at BWI.

The DEIS also provides forecasts of what ridership would be on the existing train services between Baltimore and Washington:  the Acela services (operated by Amtrak), the regular Amtrak Regional trains, and the MARC commuter service operated by the State of Maryland.  Note also that the 2045 forecasts for the train services are for both a scenario where the SCMAGLEV is not built and then what they forecast the reduced ridership would be with a SCMAGLEV option.  For the purposes here, what is of interest is the scenario with no SCMAGLEV.

The SCMAGLEV would provide a premium service, requiring 15 minutes to go between downtown Baltimore and downtown Washington, DC.  Acela also provides a premium service and currently takes 30 minutes, while the regular Amtrak Regional trains take 40 to 45 minutes and MARC service takes 60 minutes.  But the fares differ substantially.  Using the DEIS figures (with all prices and fares expressed in base year 2018 dollars), the SCMAGLEV would charge an average fare of $120 for a round-trip (Baltimore-Washington), and up to $160 for a roundtrip at peak times.  The Acela also has a high fare for its also premium service, although not as high as SCMAGLEV, charging an average of $104 for a roundtrip (using the DEIS figures).  But Amtrak Regional trains charge only $34 for a similar roundtrip, and MARC only $16.

Acela service thus provides a reasonable basis for comparison to what SCMAGLEV would provide, with the great advantage that we know now what Acela ridership has actually been.  This provides a firm base for a forecast of what Acela ridership would be in a future year in a scenario where the SCMAGLEV is not built.  And while the ridership on the two would not be exactly the same, one should expect them to be in the same ballpark.

But they are far from that:

  DEIS Forecasts of SCMAGLEV vs. Acela Ridership, Annual Trips in 2045

Route

SCMAGLEV Trips

Acela Trips

Ratio

Baltimore – DC only

19,277,578

125,226

154 times as much

All, including BWI

24,938,652

187,887

133 times as much

Sources:  DEIS, Main Report Table 4.2-3; and Table D-4-48 of Appendix D.4 of the DEIS

Using estimates just from the DEIS, the project sponsor is forecasting that annual (one-way) trips on the SCMAGLEV in 2045 would be 133 times what they would be in that year on the Acela (in a scenario where the SCMAGLEV is not built).  And it would be 154 times as much for the Baltimore – Washington riders only.  This is nonsense.  One could have a reasonable debate if the SCMAGLEV figures were twice as high, and maybe even if they were three times as high.  But it is absurd that they would be 133 or 154 times as high.

And it gets worse.  The figures above are all taken from the DEIS.  But the base year Acela ridership figures in the DEIS (Appendix D.4, Table D.4-45) differ substantially from figures Amtrak itself has produced in its recent NEC FUTURE study.  This review of future investment options in Northeast Corridor (Washington to Boston) Amtrak service was concluded in July 2017.  As part of this it provided forecasts of what future Acela ridership would be under various alternatives, including one (its Alternative 3) where Acela trains would be substantially upgraded and require just 20 minutes for the trip between downtown Baltimore and downtown Washington, DC.  This would be quite similar to what SCMAGLEV service would be.

But for reasons that are not clear, the base year figures for Acela ridership between Baltimore and Washington differ substantially between what the SCMAGLEV DEIS has and what NEC FUTURE has.  The figure in the NEC FUTURE study (for a base year of 2013) puts the number of riders (one-way) between Baltimore and Washington (and not counting those who boarded north of Baltimore, at Philadelphia or New York for example, and then rode through to Washington, and similarly for those going from Washington to Baltimore) at just 17,595.  The DEIS for the SCMAGLEV put the similar Acela ridership (for a base year of 2017) at 147,831 (calculated from Table D.4-45, of Appendix D.4).  While the base years differ (2013 vs. 2017), the disparity cannot be explained by that.  It is far too large.  My guess would be that the DEIS counted all Acela travelers taking up seats between Baltimore and Washington, including those who alighted north of Baltimore (or whose destination from Washington was north of Baltimore), and not just those travelers traveling solely between Washington and Baltimore.  But the SCMAGLEV will be serving only the Baltimore-Washington market, with no interconnections with the train routes coming from north of Baltimore.

What was the source of the Acela ridership figure in the DEIS of 147,831 in 2017?  That is not clear.  Table D.4-45 of Appendix D.4 says that its source is Table 3-10 of the “SCMAGLEV Final Ridership Report”, dated November 8, 2018.  But that report, which is available along with the other DEIS reports (with a direct link at https://bwmaglev.info/index.php/component/jdownloads/?task=download.send&id=71&catid=6&m=0&Itemid=101), does not have a Table 3-10.  Significant portions of that report were redacted, but in its Table of Contents no reference is shown to a Table 3-10 (even though other redacted tables, such as Tables 5-2 and 6-3, are still referenced in the Table of Contents, but labeled as redacted).

One can only speculate on why there is no Table 3-10 in the Final Ridership Report.  Perhaps it was deleted when someone discovered that the figures reported there, which were then later used as part of the database for the ridership forecast models, were grossly out of line with the Amtrak figures.  The Amtrak figure for Acela ridership for Baltimore-Washington passengers of 17,595 (in 2013) is less than one-eighth of the figure on Acela ridership shown in the DEIS or 147,831 (in 2017).

It can be difficult for an outsider to know how many of those riding on the Acela between Washington and Baltimore are passengers going just between those two cities (as well as BWI).  Most of the passengers riding on that segment will be going on to (or coming from) cities further north.  One would need access to ticket sales data.  But it is reasonable to assume that Amtrak itself would know this, and therefore that the figures in the NEC FUTURE study would likely be accurate.  Furthermore, in the forecast horizon years, where Amtrak is trying to show what Acela (and other rail) ridership would grow to with alternative investment programs, it is reasonable to assume that Amtrak would provide relatively optimistic (i.e. higher) estimates, as higher estimates are more likely to convince Congress to provide the funding that would be required for such investments.

The Amtrak figures would in any case provide a suitable comparison to what SCMAGLEV’s future ridership might be.  The Amtrak forecasts are for 2040, so for the SCMAGLEV forecasts I interpolated to produce an estimate for 2040 assuming a constant rate of growth between the forecast SCMAGLEV ridership in 2030 and that for 2045.  Both the NEC FUTURE and SCMAGLEV figures include the stop at BWI.

    Forecasts of SCMAGLEV (DEIS) vs. Acela (NEC FUTURE) Ridership between Baltimore and Washington, Annual Trips in 2040 

Alternative

SCMAGLEV Trips

Acela Trips

Ratio

No Action

22,761,428

26,177

870 times as much

Alternative 1

22,761,428

26,779

850 times as much

Alternative 2

22,761,428

29,170

780 times as much

Alternative 3

22,761,428

31,291

727 times as much

Sources:  SCMAGLEV trips interpolated from figures on forecast ridership in 2030 and 2045 (Camden Yards) in Table 4.2-3 of DEIS.  Acela trips from NEC FUTURE Final EIS, Volume 2, Appendix B.08.

The Acela ridership figures are those estimated under various investment scenarios in the rail service in the Northeast Corridor.  NEC FUTURE examined a “No Action” scenario with just minimal investments, and then various alternative investment levels to produce increasingly capable services.  Alternative 3 (of which there were four sub-variants, but all addressing alternative investments between New York and Boston and thus not affecting directly the Washington-Baltimore route) would upgrade Acela service to the extent that it would go between Baltimore and Washington in just 20 minutes.  This would be very close to the 15 minutes for the SCMAGLEV.  Yet even with such a comparable service, the SCMAGLEV DEIS is forecasting that its service would carry 727 times as many riders as what Amtrak has forecast for its Acela service (in a scenario where there is no SCMAGLEV).  This is complete nonsense.

To be clear, I would stress again that the forecast future Acela ridership figures are a scenario under various possible investment programs by Amtrak.  The investment program in Alternative 3 would upgrade Acela service to a degree where the Baltimore – Washington trip (with a stop at BWI) would take just 20 minutes.  The NEC FUTURE study forecasts that in such a scenario the Baltimore-Washington ridership on Acela would total a bit over 31,000 trips in the year 2040.  In contrast, the DEIS for the SCMAGLEV forecasts that there would in that year be close to 23 million trips taken on the similar SCMAGLEV service, requiring 15 minutes to make such a trip.  Such a disparity makes no sense.

C.  How Could the Forecasts be so Wrong?

A well-known consulting firm, Louis Berger, prepared the ridership forecasts, and their “Final Ridership Report” dated November 8, 2018, referenced above, provides an overview on the approach they took.  Unfortunately, while I appreciate that the project sponsor provided a link to this report along with the rest of the DEIS (I had asked for this, having seen references to it in the DEIS), the report that was posted had significant sections redacted.  Due to those redactions, and possibly also limitations in what the full report itself might have included (such as summaries of the underlying data), it is impossible to say for sure why the forecasts of SCMAGLEV ridership were close to three orders of magnitude greater than what ridership has been and is expected to be on comparable Acela service.

Thus I can only speculate.  But there are several indications of what may have led the SCMAGLEV estimates to be so out of line with ridership on a service that is at least broadly comparable.  Specifically:

1)  As noted above, there were apparent problems in assembling existing data on rail ridership for the Baltimore-Washington market, in particular for the Acela.  The ridership numbers for the Acela in the DEIS were more than eight times higher in their base year (2017) than what Amtrak had in an only slightly earlier base year (2013).  The ridership numbers on Amtrak Regional trains (for Baltimore-Washington riders) were closer but still substantially different:  409,671 in Table D.4-45 of the DEIS (for 2017), vs. 172,151 in NEC FUTURE (for 2013).

Table D.4-45 states that its source for this data on rail ridership is a Table 3-10 in the Final Ridership Report of November 8, 2018.  But as noted previously, such a table is not there – it was either never there or it was redacted.  Thus it is impossible to determine why their figures differ so much from those of Amtrak.  But the differences for the Acela figures (more than a factor of eight) are huge, i.e. close to an order of magnitude by itself.  While it is impossible to say for sure, my guess (as noted above) is that the Acela ridership numbers in the DEIS included travelers whose trip began, or would end, in destinations north of Baltimore, who then traveled through Baltimore on their way to, or from, Washington, DC.  But such travelers are not part of the market the SCMAGLEV would serve.

2)  In modeling the choice those traveling between Baltimore and Washington would have between SCMAGLEV and alternatives, the analysts collapsed all the train options (Acela, Amtrak Regional, and MARC) into one.  See page 61 of the Ridership Report.  They create a weighted average for a single “train” alternative, and they note that since (in their figures) MARC ridership makes up almost 90% of the rail market, the weighted averages for travel time and the fare will be essentially that of MARC.

Thus they never looked at Acela as an alternative, with a service level not far from that of SCMAGLEV.  Nor do they even consider the question of why so many MARC riders (67.5% of MARC riders in 2045 if the Camden Yards option is chosen – see page D-56 of Appendix D-4 of the DEIS) are forecast to divert to the SCMAGLEV, but are not doing so now (nor in the future) to Acela.  According to Table D-45 of Appendix D.4 of the DEIS, in their data for their 2017 base year, there are 28 times as many MARC riders as on Acela between downtown Baltimore and downtown Washington, and 20 times as many with those going to and from the BWI stop included.  Evidently, they do not find the Acela option attractive.  Why should they then find the SCMAGLEV train attractive?

3)  The answer as to why MARC riders have not chosen to ride on the Acela almost certainly has something to do with the difference in the fares.  A round-trip on MARC costs $16 a day.  A round trip on Acela costs, according to the DEIS, an average of $104 a day.  That is not a small difference.  For someone commuting 5 days a week and 50 weeks a year (or 250 days a year), the annual cost on MARC would be $4,000 but $26,000 a year on the Acela.  And it would be an even higher $30,000 a year on the SCMAGLEV (based on an average fare of $120 for a round trip), and $40,000 a year ($160 a day) at peak hours (which would cover the times commuters would normally use).  Even for those moderately well off, $40,000 a year for commuting would be a significant expense, and not an attractive alternative to MARC with its cost of just one-tenth of this.

If such costs were properly taken into account in the forecasting model, why did it nonetheless predict that most MARC riders would switch to the SCMAGLEV?  This is not fully clear as the model details were not presented in the redacted report, but note that the modelers assigned high dollar amounts for the time value of money ($31.00 to $46.50 for commuters and other non-business travel, and $50.60 to $75.80 for business travel – see page 53 of the Ridership Report).  However, even at such high values, the numbers do not appear to be consistent.  Taking a SCMAGLEV (15 minute trip) rather than MARC (60 minutes) would save 45 minutes each way or 1 1/2 hours a day.  Only at the very high end value of time for business travelers (of $75.80 per hour, or $113.70 for 1 1/2 hours) would this value of time offset the fare difference of $104 (using the average SCMAGLEV fare of $120 minus the MARC fare of $16).  And even that would not suffice for travelers at peak hours (with its SCMAGLEV fare of $160).

But there is also a more basic problem.  It is wrong to assume that travelers on MARC treat their 60 minutes on the train as all wasted time.  They can read, do some work, check their emails, get some sleep, or plan their day.  The presumption that they would pay amounts similar to what some might on average earn in an hour based on their annual salaries is simply incorrect.  And as noted above, if it were correct, then one would see many more riders on the Acela than one does (and similarly riders on the Amtrak Regional trains, that require about 40 minutes for the Washington to Baltimore trip, with an average fare of $34 for a round trip).

There is a similar issue for those who drive.  Those who drive do not place a value on the time spent in their cars equal to what they would earn in an hourly equivalent of their regular salary.  They may well want to avoid traffic jams, which are stressful and frustrating for other reasons, but numerous studies have found that a simple value-of-time calculation based on annual salaries does not explain why so many commuters choose to drive.

4)  Data for the forecasting model also came in part from two personal surveys.  One was an in-person survey of travelers encountered on MARC, at either the MARC BWI Station or onboard Penn Line trains, or at BWI airport.  The other was an online internet survey, where they unfortunately redacted out how they chose possible respondents.

But such surveys are unreliable, with answers that depend critically on how the questions are phrased.  The Final Ridership report does not include the questionnaire itself (most such reports would), so one cannot know what bias there might have been in how the questions were worded.  As an example (and admittedly an exaggerated example, to make the point) were the MARC riders simply asked whether they would prefer a much faster, 15 minute, trip?  Or were they asked whether they would pay an extra $104 per day ($144 at peak hours) to ride a service that would save them 45 minutes each way on the train?

But even such willingness to pay questions are notoriously unreliable.  An appropriate follow-up question to a MARC rider saying they would be willing to pay up to an extra $144 a day to ride a SCMAGLEV, would be why are they evidently not now riding the Acela (at an extra $88 a day) for a ride just 15 minutes longer than what it would be on the SCMAGLEV.

One therefore has to be careful in interpreting and using the results from such a survey in forecasting how travelers would behave.  If current choices (e.g. using the MARC rather than the Acela) do not reflect the responses provided, one should be concerned.

5)  Finally, the particular mathematical form used to model the choices the future travelers would make can make a big difference to the findings.  The Final Ridership Report briefly explains (page 53) that it used a multinomial logit model as the basis for its modeling.  Logit functions assign a continuous probability (starting from 0 and rising to 100%) of some event occurring.  In this model, the event is that a traveler going from one travel zone to another will choose to travel via the SCMAGLEV, or not.  The likelihood of choosing to travel via the SCMAGLEV will be depicted as an S-shaped function, starting at zero and then smoothly rising (following the S-shape) until it reaches 100%, depending on, among other factors, what the travel time savings might be.

The results that such a model will predict will depend critically, of course, on the particular parameters chosen.  But the heavily redacted Final Ridership Report does not show what those parameters were nor how they were chosen or possibly estimated, nor even the complete set of variables used in that function.  The report says little (in what remains after the redactions) beyond that they used that functional form.

A feature of such logit models is that while the choices are discrete (one either will ride the SCMAGLEV or will not), it allows for “fuzziness” around the turning points, that recognize that between individuals, even if they confront a similar combination of variables (a combination of cost, travel time, and other measured attributes), some will simply prefer to drive while some will prefer to take the train.  That is how people are.  But then, while a higher share might prefer to take a train (or the SCMAGLEV) when travel times fall (by close to 45 minutes with the SCMAGLEV when compared to their single “train” option that is 90% MARC, and by variable amounts for those who drive depending on the travel zone pairs), how much higher that share will be will depend on the parameters they selected for their logit.

With certain parameters, the responses can be sensitive to even small reductions in travel times, and the predicted resulting shifts then large.  But are those parameters reasonable?  As noted previously, a test would have been whether the model, with the parameters chosen, would have predicted accurately the number of riders actually observed on the Acela trains in the base year.  But it does not appear such a test was done.  At least no such results were reported to test whether the model was validated or not.

Thus there are a number of possible reasons why the forecast ridership on the SCMAGLEV differs so much from what one currently observes for ridership on the Acela, and from what one might reasonably expect Acela ridership to be in the future.  It is not possible to say whether these are indeed the reasons why the SCMAGLEV forecasts are so incredibly out of line with what one observes for the Acela.  There may be, and indeed likely are, other reasons as well.  But due to issues such as those outlined here, one can understand the possible factors behind SCMAGLEV ridership forecasts that deviate so markedly from plausibility.

D.  Conclusion

The ridership forecasts for the SCMAGLEV are vastly over-estimated.  Predicted ridership on the SCMAGLEV is a minimum of two, and up to three, orders of magnitude higher than what has been observed on, and can reasonably be forecast for, the Acela.  One should not be getting predicted ridership that is more than 100 times what one observes on a comparable, existing (and thus knowable), service.

With ridership on the proposed system far less than what the project sponsors have forecast, the case for building the SCMAGLEV collapses.  Operational and maintenance costs would not be covered, much less any possibility of paying back a portion of the billions of dollars spent to build it, nor will the purported economic benefits follow.

However, the harm to the environment will have been done.  Even if the system is then shut down (due to the forecast ridership never materializing), it will not be possible to reverse much of that environmental damage.

The US very much needs to improve its public transit.  It is far too difficult, with resulting harm both to the economy and to the population, to move around in the Baltimore-Washington region.  But fixing this will require a focus on the basic nuts and bolts of operating, maintaining, and investing in the transit systems we have, including the trains and buses.  This might not look as attractive as a magnetically levitating train, but will be of benefit.  And it will be of benefit to the general public – in particular to those who rely on public transit – and not just to a narrow elite that can afford $120 fares.  Money for public transit is scarce.  It should not be wasted on shiny new toys.

The Increasingly Attractive Economics of Solar Power: Solar Prices Have Plunged

A.  Introduction

The cost of solar photovoltaic power has fallen dramatically over the past decade, and it is now, together with wind, a lower cost source of new power generation than either fossil-fuel (coal or gas) or nuclear power plants.  The power generated by a new natural gas-fueled power plant in 2018 would have cost a third more than from a solar or wind plant (in terms of the price they would need to sell the power for in order to break even); coal would have cost 2.4 times as much as solar or wind; and a nuclear plant would have cost 3.5 times as much.

These estimates (shown in the chart above, and discussed in more detail below) were derived from figures estimated by Lazard, the investment bank, and are based on bottom-up estimates of what such facilities would have cost to build and operate, including the fuel costs.  But one also finds a similar sharp fall in solar energy prices in the actual market prices that have been charged for the sale of power from such plants under long-term “power purchase agreements” (PPAs).  These will also be discussed below.

With the costs where they are now, it would not make economic sense to build new coal or nuclear generation capacity, nor even gas in most cases.  In practice, however, the situation is more complex due to regulatory issues and conflicting taxes and subsidies, and also because of variation across regions.  Time of day issues may also enter, depending on when (day or night) the increment in new capacity might be needed.  The figures above are also averages, particular cases vary, and what is most economic in any specific locale will depend on local conditions.  Nevertheless, and as we will examine below, there has been a major shift in new generation capacity towards solar and wind, and away from coal (with old coal plants being retired) and from nuclear (with no new plants being built, but old ones largely remaining).

But natural gas generation remains large.  Indeed, while solar and wind generation have grown quickly (from a low base), and together account for the largest increment in new power capacity in recent years, gas accounts for the largest increment in power production (in megawatt-hours) measured from the beginning of this decade.  Why?  In part this is due to the inherent constraints of solar and wind technologies:  Solar panels can only generate power when the sun shines, and wind turbines when the wind is blowing.  But more interestingly, one also needs to look at the economics behind the choice as to whether or not to build new generation capacity to replace existing capacity, and then what sources of capacity to use.  Critical is what economists call the marginal cost of such production.  A power plant lasts for many years once it is built, and the decision on whether to keep an existing plant in operation for another year depends only on the cost of operating and maintaining the plant.  The capital cost has already been spent and is no longer relevant to that decision.

Details in the Lazard report can be used to derive such marginal cost estimates by power source, and we will examine these below.  While the Lazard figures apply to newly built plants (older plants will generally have higher operational and maintenance costs, both because they are getting old and because technology was less efficient when they were built), the estimates based on new plants can still give us a sense of these costs.  But one should recognize they will be biased towards indicating the costs of the older plants are lower than they in fact are.  However, even these numbers (biased in underestimating the costs of older plants) imply that it is now more economical to build new wind and possibly solar plants, in suitable locales, than it costs to continue to keep open and operate coal-burning power plants.  This will be especially true for the older, less-efficient, coal-burning plants.  Thus we should be seeing old coal-burning plants being shut down.  And indeed we do.  Moreover, while the costs of building new wind and solar plants are not yet below the marginal costs of keeping open existing gas-fueled and nuclear power plants, they are on the cusp of being so.

These costs also do not reflect any special subsidies that solar and wind plants might benefit from.  These vary by state.  Fossil-fueled and nuclear power plants also enjoy subsidies (often through special tax advantages), but these are long-standing and are implicitly being included in the Lazard estimates of the costs of such traditional plants.

But one special subsidy enjoyed by fossil fuel burning power plants, not reflected in the Lazard cost estimates, is the implicit subsidy granted to such plants from not having to cover the cost of the damage from the pollution they generate.  Those costs are instead borne by the general public.  And while such plants pollute in many different ways (especially the coal-burning ones), I will focus here on just one of those ways – their emissions of greenhouse gases that are leading to a warming planet and consequent more frequent and more damaging extreme weather events.  Solar and wind generation of power do not cause such pollution – the burning of coal and gas do.

To account for such costs and to ensure a level playing field between power sources, a fee would need to be charged to reflect the costs being imposed on the general population from this (and indeed other) such pollution.  The revenues generated could be distributed back to the public in equal per capita terms, as discussed in an earlier post on this blog.  We will see that a fee of even just $20 per ton of CO2 emitted would suffice to make it economic to build new solar and wind power plants to substitute not just for new gas and coal burning plants, but for existing ones as well.  Gas and especially coal burning plants would not be competitive with installing new solar or wind generation if they had to pay for the damage done as a result of their greenhouse gas pollution, even on just marginal operating costs.

Two notes before starting:  First, many will note that while solar might be fine for the daytime, it will not be available at night.  Similarly, wind generation will be fine when the wind blows, but it may not always blow even in the windiest locales.  This is of course true, and should solar and wind capacity grow to dominate power generation, there will have to be ways to store that power to bridge the times from when the generation occurs to when the power is used.

But while storage might one day be an issue, it is mostly not an issue now.  In 2018, utility-scale solar only accounted for 1.6% of power generation in the US (and 2.3% if one includes small scale roof-top systems), while wind only accounted for 6.6%.  At such low shares, solar and wind power can simply substitute for other, higher cost, sources of power (such as from coal) during the periods the clean sources are available.  Note also that the cost figures for solar and wind reflected in the chart at the top of this post (and discussed in detail below) take into account that solar and wind cannot be used 100% of the time.  Rather, utilization is assumed to be similar to what their recent actual utilization has been, not only for solar and wind but also for gas, coal and nuclear.  Solar and wind are cheaper than other sources of power (over the lifetime of these investments) despite their inherent constraints on possible utilization.

But where the storage question can enter is in cases where new generation capacity is required specifically to serve evening or night-time needs.  New gas burning plants might then be needed to serve such time-of-day needs if storage of day-time solar is not an economic option.  And once such gas-burning plants are built, the decision on whether they should be run also to serve day-time needs will depend on a comparison of the marginal cost of running these gas plants also during the day, to the full cost of building new solar generation capacity, as was discussed briefly above and will be considered in more detail below.

This may explain, in part, why we see new gas-burning plants still being built nationally.  While less than new solar and wind plants combined (in terms of generation capacity), such new gas-burning plants are still being built despite their higher cost.

More broadly, California and Hawaii (both with solar now accounting for over 12% of power used in those states) are two states (and the only two states) which may be approaching the natural limits of solar generation in the absence of major storage.  During some sunny days the cost of power is being driven down to close to zero (and indeed to negative levels on a few days).  Major storage will be needed in those states (and only those states) to make it possible to extend solar generation much further than where it is now.  But this should not be seen so much as a “problem” but rather as an opportunity:  What can we do to take advantage of cheap day-time power to make it available at all hours of the day?  I hope to address that issue in a future blog post.  But in this blog post I will focus on the economics of solar generation (and to a lesser extent from wind), in the absence of significant storage.

Second, on nomenclature:  A megawatt-hour is a million watts of electric power being produced or used for one hour.  One will see it abbreviated in many different ways, including MWHr, MWhr, MWHR, MWH, MWh, and probably more.  I will try to use consistently MWHr.  A kilowatt-hour (often kWh) is a thousand watts of power for one hour, and is the typical unit used for homes.  A megawatt-hour will thus be one thousand times a kilowatt-hour, so a price of, for example, $20 per MWHr for solar-generated power (which we will see below has in fact been offered in several recent PPA contracts) will be equivalent to 2.0 cents per kWh.  This will be the wholesale price of such power.  The retail price in the US for households is typically around 10 to 12 cents per kWh.

B.  The Levelized Cost of Energy 

As seen in the chart at the top of this post, the cost of generating power by way of new utility-scale solar photovoltaic panels has fallen dramatically over the past decade, with a cost now similar to that from new on-shore wind turbines, and well below the cost from building new gas, coal, or nuclear power plants.  These costs can be compared in terms of the “levelized cost of energy” (LCOE), which is an estimate of the price that would need to be charged for power from such a plant over its lifetime, sufficient to cover the initial capital cost (at the anticipated utilization rate), plus the cost of operating and maintaining the plant,

Lazard, the investment bank, has published estimates of such LCOEs annually for some time now.  The most recent report, issued in November 2018, is version 12.0.  Lazard approaches the issue as an investment bank would, examining the cost of producing power by each of the alternative sources, with consistent assumptions on financing (with a debt/equity ratio of 60/40, an assumed cost of debt of 8%, and a cost of equity of 12%) and a time horizon of 20 years.  They also include the impact of taxes, and show separately the impact of special federal tax subsidies for clean energy sources.  But the figures I will refer to throughout this post (including in the chart above) are always the estimates excluding any impact from special subsidies for clean energy.  The aim is to see what the underlying actual costs are, and how they have changed over time.

The Lazard LCOE estimates are calculated and presented in nominal terms.  They show the price, in $/MWHr, that would need to be charged over a 20-year time horizon for such a project to break even.  For comparability over time, as well as to produce estimates that can be compared directly to the PPA contract prices that I will discuss below, I have converted those prices from nominal to real terms in constant 2017 dollars.  Two steps are involved.  First, the fixed nominal LCOE prices over 20 years will be falling over time in real terms due to general inflation.  They were adjusted to the prices of their respective initial year (i.e. the relevant year from 2009 to 2018) using an inflation rate of 2.25% (which is the rate used for the PPA figures discussed below, the rate the EIA assumed in its 2018 Annual Energy Outlook report, and the rate which appears also to be what Lazard assumed for general cost escalation factors).  Second, those prices for the years between 2009 and 2018 were all then converted to constant 2017 prices based on actual inflation between those years and 2017.

The result is the chart shown at the top of this post.  The LCOEs in 2018 (in 2017$) were $33 per MWHr for a newly built utility-scale solar photovoltaic system and also for an on-shore wind installation, $44 per MWHr for a new natural gas combined cycle plant, $78 for a new coal-burning plant, and $115 for a new nuclear power plant.  The natural gas plant would cost one-third more than a solar or wind plant, coal would cost 2.4 times as much, and a nuclear plant 3.5 times as much.  Note also that since the adjustments for inflation are the same for each of the power generation methods, their costs relative to each other (in ratio terms) are the same for the LCOEs expressed in nominal cost terms.  And it is their costs relative to each other which most matters.

The solar prices have fallen especially dramatically.  The 2018 LCOE was only one-tenth of what it was in 2009.  The cost of wind generation has also fallen sharply over the period, to about one-quarter in 2018 of what it was in 2009.  The cost from gas combined cycle plants (the most efficient gas technology, and is now widely used) also fell, but only by about 40%, while the cost of coal or nuclear were roughly flat or rising, depending on precisely what time period is used.

There is good reason to believe the cost of solar technology will continue to decline.  It is still a relatively new technology, and work in labs around the world are developing solar technologies that are both more efficient and less costly to manufacture and install.

Current solar installations (based on crystalline silicon technology) will typically have conversion efficiencies of 15 to 17%.  And panels with efficiencies of up to 22% are now available in the market – a gain already on the order of 30 to 45% over the 15 to 17% efficiency of current systems.  But a chart of how solar efficiencies have improved over time (in laboratory settings) shows there is good reason to believe that the efficiencies of commercially available systems will continue to improve in the years to come.  While there are theoretical upper limits, labs have developed solar cell technologies with efficiencies as high as 46% (as of January 2019).

Particularly exciting in recent years has been the development of what are called “perovskite” solar technologies.  While their current efficiencies (of up to 28%, for a tandem cell) are just modestly better than purely crystalline silicon solar cells, they have achieved this in work spanning only half a decade.  Crystalline silicon cells only saw such an improvement in efficiencies in research that spanned more than four decades.  And perhaps more importantly, perovskite cells are much simpler to manufacture, and hence much cheaper.

Based on such technologies, one could see solar efficiencies doubling within a few years, from the current 15 to 17% to say 30 to 35%.  And with a doubling in efficiency, one will need only half as many solar panels to produce the same megawatts of power, and thus also only half as many frames to hold the panels, half as much wiring to link them together, and half as much land.  Coupled with simplified and hence cheaper manufacturing processes (such as is possible for perovskite cells), there is every reason to believe prices will continue to fall.

While there can be no certainty in precisely how this will develop, a simple extrapolation of recent cost trends can give an indication of what might come.  Assuming costs continue to change at the same annual rate that they had over the most recent five years (2013 to 2018), one would find for the years up to 2023:

If these trends hold, then the LCOE (in 2017$) of solar power will have fallen to $13 per MWHr by 2023, wind will have fallen to $18, and gas will be at $32 (or 2.5 times the LCOE of solar in that year, and 80% above the LCOE of wind).  And coal (at $70) and nuclear (at $153) will be totally uncompetitive.

This is an important transition.  With the dramatic declines in the past decade in the costs for solar power plants, and to a lesser extent wind, these clean sources of power are now more cost competitive than traditional, polluting, sources.  And this is all without any special subsidies for the clean energy.  But before looking at the implications of this for power generation, as a reality check it is good first to examine whether the declining costs of solar power have been reflected in actual market prices for such power.  We will see that they have.

C.  The Market Prices for Solar Generated Power

Power Purchase Agreements (PPAs) are long-term contracts where a power generator (typically an independent power producer) agrees to supply electric power at some contracted capacity and at some price to a purchaser (typically a power utility or electric grid operator).  These are competitively determined (different parties interested in building new power plants will bid for such contracts, with the lowest price winning) and are a direct market measure of the cost of energy from such a source.

The Lawrence Berkeley National Lab, under a contract with the US Department of Energy, produces an annual report that reviews and summarizes PPA contracts for recent utility-scale solar power projects, including the agreed prices for the power.  The most recent was published in September 2018, and covers 2018 (partially) and before.  While the report covers both solar photovoltaic and concentrating solar thermal projects, the figures of interest to us here (and comparable to the Lazard LCOEs discussed above) are the PPAs for the solar photovoltaic projects.

The PPA prices provided in the report were all calculated by the authors on a levelized basis and in terms of 2017 prices.  This was done to put them all on a comparable basis to each other, as the contractual terms of the specific contracts could differ (e.g. some had price escalation clauses and some did not).  Averages by year were worked out with the different projects weighted by generation capacity.

The PPA prices are presented by the year the contracts were signed.  If one then plots these PPA prices with a one year lag and compare them to the Lazard estimated LCOE prices of that year, one finds a remarkable degree of overlap:

This high degree of overlap is extraordinary.  Only the average PPA price for 2010 (reflecting the 2009 average price lagged one year) is off, but would have been close with a one and a half year lag rather than a one year lag.  Note also that while the Lawrence Berkeley report has PPA prices going back to 2006, the figures for the first several years are based on extremely small samples (just one project in 2006, one in 2007, and three in 2008, before rising to 16 in 2009 and 30 in 2010).  For that reason I have not plotted the 2006 to 2008 PPA prices (which would have been 2007 to 2009 if lagged one year), but they also would have been below the Lazard LCOE curve.

What might be behind this extraordinary overlap when the PPA prices are lagged one year?  Two possible explanations present themselves.  One is that the power producers when making their PPA bids realize that there will be a lag from when the bids are prepared to when the winning bidder is announced and construction of the project begins.  With the costs of solar generation falling so quickly, it is possible that the PPA bids reflect what they know will be a lag between when the bid is prepared and when the project has to be built (with solar panels purchased and other costs incurred).  If that lag is one year, one will see overlap such as that found for the two curves.

Another possible explanation for the one-year shift observed between the PPA prices (by date of contract signing) and the Lazard LCOE figures is that the Lazard estimates labeled for some year (2018 for example) might in fact represent data on the cost of the technologies as of the prior year (2017 in this example).  One cannot be sure from what they report.  Or the remarkable degree of overlap might be a result of some combination of these two possible explanations, or something else.

But for whatever reason, the two estimates move almost exactly in parallel over time, and hence show an almost identical rate of decline for both the cost of generating power from solar photovoltaic sources and in the market PPA prices for such power.  And it is that rapid rate of decline which is important.

It is also worth noting that the “bump up” in the average PPA price curve in 2017 (shown in the chart as 2018 with the one year lag) reflects in part that a significant number of the projects in the 2017 sample of PPAs included, as part of the contract, a power storage component to store a portion of the solar-generated power for use in the evening or night.  But these additional costs for storage were remarkably modest, and were even less in several projects in the partial-year 2018 sample.  Specifically, Nevada Energy (as the offtaker) announced in June 2018 that it had contracted for three major solar projects that would include storage of power of up to one-quarter of generation capacity for four hours, with overall PPA prices (levelized, in 2017 prices) for both the generation and the storage of just $22.8, $23.5, and $26.4 per MWHr (i.e. 2.28 cents, 2.35 cents, and 2.64 cents per kWh, respectively).

The PPA prices reported can also be used to examine how the prices vary by region.  One should expect solar power to be cheaper in southern latitudes than in northern ones, and in dry, sunny, desert areas than in regions with more extensive cloud cover.  And this has led to the criticism by skeptics that solar power can only be competitive in places such as the US Southwest.

But this is less of an issue than one might assume.  Dividing up the PPA contracts by region (with no one-year lag in this chart), one finds:

Prices found in the PPAs are indeed lower in the Southwest, California, and Texas.  But the PPA prices for projects in the Southeast, the Midwest, and the Northwest fell at a similar pace as those in the more advantageous regions (and indeed, at a more rapid pace up to 2014).  And note that the prices in those less advantageous regions are similar to what they were in the more advantageous regions just a year or two before.  Finally, the absolute differences in prices have become relatively modest in the last few years.

The observed market prices for power generated by solar photovoltaic systems therefore appear to be consistent with the bottom-up LCOE estimates of Lazard – indeed remarkably so.  Both show a sharp fall in solar energy prices/costs over the last decade, and sharp falls both for the US as a whole and by region.  The next question is whether we see this reflected in investment in additions to new power generation capacity, and in the power generated by that capacity.

D.  Additions to Power Generation Capacity, and in Power Generation

The cost of power from a new solar or wind plant is now below the cost from gas (while the cost of new coal or nuclear generation capacity is totally uncompetitive).  But the LCOEs indicate that the cost advantage relative to gas is relatively recent in the case of solar (starting from 2016), and while a bit longer for wind, the significant gap in favor of wind only opened up in 2014.  One needs also to recognize that these are average or mid-point estimates of costs, and that in specific cases the relative costs will vary depending on local conditions.  Thus while solar or wind power is now cheaper on average across the US, in some particular locale a gas plant might be less expensive (especially if the costs resulting from its pollution are not charged).  Finally, and as discussed above, there may be time-of-day issues that the new capacity may be needed for, with this affecting the choices made.

Thus while one should expect a shift towards solar and wind over the last several years, and away from traditional fuels, the shift will not be absolute and immediate.  What do we see?

First, in terms of the gross additions to power sector generating capacity:

The chart shows the gross additions to power capacity, in megawatts, with both historical figures (up through 2018) and as reflected in plans filed with the US Department of Energy (for 2019 and 2020, with the plans as filed as of end-2018).  The data for this (and the other charts in this section) come from the most recent release of the Electric Power Annual of the Energy Information Agency (EIA) (which was for 2017, and was released on October 22, 2018), plus from the Electric Power Monthly of February, 2019, also from the Energy Information Agency (where the February issue each year provides complete data for the prior calendar year, i.e. for 2018 in this case).

The planned additions to capacity (2019 and 2020 in the chart) provide an indication of what might happen over the next few years, but must be interpreted cautiously.  While probably pretty good for the next few years, biases will start to enter as one goes further into the future.  Power producers are required to file their plans for new capacity (as well as for retirements of existing capacity) with the Department of Energy, for transparency and to help ensure capacity (locally as well as nationally) remains adequate.  But these reported plans should be approached cautiously.  There is a bias as projects that require a relatively long lead time (such as gas plants, as well as coal and especially nuclear) will be filed years ahead, while the more flexible, shorter construction periods, required for solar and wind plants means that these plans will only be filed with the Department of Energy close to when that capacity will be built.  But for the next few years, the plans should provide an indication of how the market is developing.

As seen in the chart, solar and wind taken together accounted for the largest single share of gross additions to capacity, at least through 2017.  While there was then a bump up in new gas generation capacity in 2018, this is expected to fall back to earlier levels in 2019 and 2020.  And these three sources (solar, wind, and gas) accounted for almost all (93%) of the gross additions to new capacity over 2012 to 2018, with this expected to continue.

New coal-burning plants, in contrast, were already low and falling in 2012 and 2013, and there have been no new ones since then.  Nor are any planned.  This is as one would expect based on the LCOE estimates discussed above – new coal plants are simply not cost competitive.  And the additions to nuclear and other capacity have also been low.  “Other” capacity is a miscellaneous category that includes hydro, petroleum-fueled plants such as diesel, as well as other renewables such as from the burning of waste or biomass. The one bump up, in 2016, is due to a nuclear power plant coming on-line that year.  It was unit #2 of the Watts Bar nuclear power plant built by the Tennessee Valley Authority (TVA), and had been under construction for decades.  Indeed the most recent nuclear plant completed in the US before this one was unit #1 at the same TVA plant, which came on-line 20 years before in 1996.  Even aside from any nuclear safety concerns, nuclear plants are simply not economically competitive with other sources of power.

The above are gross additions to power generating capacity, reflecting what new plants are being built.  But old, economically or technologically obsolete, plants are also being retired, so what matters to the overall shift in power generation capacity is what has happened to net generation capacity:

What stands out here is the retirement of coal-burning plants.  And while the retirements might appear to diminish in the plans going forward, this may largely be due to retirement plans only being announced shortly before they happen.  It is also possible that political pressure from the Trump administration to keep coal-burning plants open, despite their higher costs (and their much higher pollution), might be a factor.  We will see what happens.

The cumulative impact of these net additions to capacity (relative to 2010 as the base year) yields:

Solar plus wind accounts for the largest addition to capacity, followed by gas.  Indeed, each of these accounts for more than 100% of the growth in overall capacity, as there has been a net reduction in the nuclear plus other category, and especially in coal.

But what does this mean in terms of the change in the mix of electric power generation capacity in the US?  Actually, less than one might have thought, as one can see in a chart of the shares:

The share of coal has come down, but remains high, and similarly for nuclear (plus miscellaneous other) capacity.  Gas remains the highest and has risen as a share, while solar and wind, while rising at a rapid pace relative to where it was to start, remains the smallest shares (of the categories used here).

The reason for these relatively modest changes in shares is that while solar and wind plus gas account for more than 100% of the net additions to capacity, that net addition has been pretty small.  Between 2010 and 2018, the net addition to US electric power generation capacity was just 58.8 thousand megawatts, or an increase over eight years of just 5.7% over what capacity was in 2010 (1,039.1 thousand megawatts).  A big share of something small will still be small.

So even though solar and wind are now the lowest cost sources of new power generation, the very modest increase in the total power capacity needed has meant that not that much has been built.  And much of what has been built has been in replacement of nuclear and especially coal capacity.  As we will discuss below, the economic issue then is not whether solar and wind are the cheapest source of new capacity (which they are), but whether new solar and wind are more economic than what it costs to continue to operate existing coal and nuclear plants.  That is a different question, and we will see that while new solar and wind are now starting to be a lower cost option than continuing to operate older coal (but not nuclear) plants, this development (a critically important development) has only been recent.

Why did the US require such a small increase in power generation capacity in recent years?  As seen in the chart below, it is not because GDP has not grown, but rather because energy efficiency (real GDP per MWHr of power) improved tremendously, at least until 2017:

From 2010 to 2017, real GDP rose by 15.7% (2.1% a year on average), but GDP per MWHr of power generated rose by 18.3%.  That meant that power generation (note that generation is the relevant issue here, not capacity) could fall by 2.2% despite the higher level of GDP.  Improving energy efficiency was a key priority during the Obama years, and it appears to have worked well.  It is better for efficiency to rise than to have to produce more power, even if that power comes from a clean source such as solar or wind.

This reversed direction in 2018.  It is not clear why, but might be an early indication that the policies of the Trump administration are harming efficiency in our economy.  However, this is still just one year of data, and one will need to wait to see whether this was an aberration or a start of a new, and worrisome, trend.

Which brings us to generation.  While the investment decision is whether or not to add capacity, and if so then of what form (e.g. solar or gas or whatever), what is ultimately needed is the power generated.  This depends on the capacity available and then on the decision of how much of that capacity to use to generate the power needed at any given moment.  One needs to keep in mind that power in general is not stored (other than still very limited storage of solar and wind power), but rather has to be generated at the moment needed.  And since power demand goes up and down over the course of the day (higher during the daylight hours and lower at night), as well as over the course of the year (generally higher during the summer, due to air conditioning, and lower in other seasons), one needs total generation capacity sufficient to meet whatever the peak load might be.  This means that during all other times there will be excess, unutilized, capacity.  Indeed, since one will want to have a safety margin, one will want to have total power generation capacity of even more than whatever the anticipated peak load might be in any locale.

There will always, then, be excess capacity, just sometimes more and sometimes less.  And hence decisions will be necessary as to what of the available capacity to use at any given moment.  While complex, the ultimate driver of this will be (or at least should be, in a rational system) the short-run costs of producing power from the possible alternative sources available in the region where the power is needed.  These costs will be examined in the next section below.  But for here, we will look at how generation has changed over the last several years.

In terms of the change in power generation by source relative to the levels in 2010, one finds:

Gas now accounts for the largest increment in generation over this period, with solar and wind also growing (steadily) but by significantly less.  Coal powered generation, in contrast, fell substantially, while nuclear and other sources were basically flat.  And as noted above, due to increased efficiency in the use of power (until 2017), total power use was flat to falling a bit, even as GDP grew substantially.  This reversed in 2018  when efficiency fell, and gas generated power rose to provide for the resulting increased power demands.  Solar and wind continued on the same path as before, and coal generation still fell at a similar pace as before.  But it remains to be seen whether 2018 marked a change in the previous trend in efficiency gains, or was an aberration.

Why did power generation from gas rise by more than from solar and wind over the period, despite the larger increase in solar plus wind capacity than in gas generation capacity?  In part this reflects the cost factors which we will discuss in the next section below.  But in part one needs also to recognize factors inherent in the technologies.  Solar generation can only happen during the day (and also when there is no cloud cover), while wind generation depends on when the wind blows.  Without major power storage, this will limit how much solar and wind can be used.

The extent to which some source of power is in fact used over some period (say a year), as a share of what would be generated if the power plant operated at 100% of capacity for 24 hours a day, 365 days a year, is defined as the “capacity factor”.  In 2018, the capacity factor realized for solar photovoltaic systems was 26.1% while for wind it was 37.4%.  But for no power source is it 100%.  For natural gas combined cycle plants (the primary source of gas generation), the capacity factor was 57.6% in 2018 (up from 51.3% in 2017, due to the jump in power demand in 2018).  This is well below the theoretical maximum of 100% as in general one will be operating at less than peak capacity (plus plants need to be shut down periodically for maintenance and other servicing).

Thus increments in “capacity”, as measured, will therefore not tell the whole story.  How much such capacity is used also matters.  And the capacity factors for solar and wind will in general be less than what they will be for the other primary sources of power generation, such as gas, coal, and nuclear (and excluding the special case of plants designed solely to operate for short periods of peak load times, or plants used as back-ups or for cases of emergencies).  But how much less depends only partly on the natural constraints on the clean technologies.  It also depends on marginal operating costs, as we will discuss below.

Finally, while gas plus solar and wind have grown in terms of power generation since 2010, and coal has declined (and nuclear and other sources largely unchanged), coal-fired generation remains important.  In terms of the percentage shares of overall power generation:

While coal has fallen as a share, from about 45% of US power generation in 2010 to 27% in 2018, it remains high.  Only gas is significantly higher (at 35% in 2010).  Nuclear and other sources (such as hydro) accounts for 29%, with nuclear alone accounting for two-thirds of this and other sources the remaining one-third.  Solar and wind have grown steadily, and at a rapid rate relative to where they were in 2010, but in 2018 still accounted only for about 8% of US power generation.

Thus while coal has come down, there is still very substantial room for further substitution out of coal, by either solar and wind or by natural gas.  The cost factors that will enter into this decision on substituting out of coal will be discussed next.

E.  The Cost Factors That Enter in the Decisions on What Plants to Build, What Plants to Keep in Operation, and What Plants to Use

The Lazard analysis of costs presents estimates not only for the LCOE of newly built power generation plants, but also figures that can be used to arrive at the costs of operating a plant to produce power on any given day, and of operating a plant plus keeping it maintained for a year.  One needs to know these different costs in order to address different questions.  The LCOE is used to decide whether to build a new plant and keep it in operation for a period (20 years is used); the operating cost is used to decide which particular power plant to run at any given time to generate the power then needed (from among all the plants up and available to run that day); while the operating cost plus the cost of regular annual maintenance is used in the decision of whether to keep a particular plant open for another year.

The Lazard figures are not ideal for this, as they give cost figures for a newly built plant, using the technology and efficiencies available today.  The cost to maintain and operate an older plant will be higher than this, both because older technologies were less efficient but also simply because they are older and hence more liable to break down (and hence cost more to keep running) than a new plant.  But the estimates for a new plant do give us a sense of what the floor for such costs might be – the true costs for currently existing plants of various ages will be somewhat higher.

Lazard also recognized that there will be a range of such costs for a particular type of plant, depending on the specifics of the particular location and other such factors.  Their report therefore provides both what it labels low end and high end estimates, and with a mid-point estimate then based usually on the average between the two.  The figures shown in the chart at the top of this post are the mid-point estimates, but in the tables below we will show the low and high end cost estimates as well.  These figures are helpful in providing a sense of the range in the costs one should expect, although how Lazard defined the range they used is not fully clear.  They are not of the absolutely lowest possible cost plant nor absolutely highest possible cost plant.  Rather, the low end figures appear to be averages of the costs of some share of the lowest cost plants (possibly the lowest one third), and similarly for the high end figures.

The cost figures below are from the 2018 Lazard cost estimates (the most recent year available).  The operating and maintenance costs are by their nature current expenditures, and hence their costs will be in current, i.e. 2018, prices.  The LCOE estimates of Lazard are different.  As was noted above, these are the levelized prices that would need to be charged for the power generated to cover the costs of building and then operating and maintaining the plant over its assumed (20 year) lifetime.  They therefore need to be adjusted to reflect current prices.  For the chart at the top of this post, they were put in terms of 2017 prices (to make them consistent with the PPA prices presented in the Berkeley report discussed above).  But for the purposes here, we will put them in 2018 prices to ensure consistency with the prices for the operating and maintenance costs.  The difference is small (just 2.2%).

The cost estimates derived from the Lazard figures are then:

(all costs in 2018 prices)

A.  Levelized Cost of Energy from a New Power Plant:  $/MWHr

Solar

Wind

Gas

Coal

Nuclear

low end

$31.23

$22.65

$32.02

$46.85

$87.46

mid-point

$33.58

$33.19

$44.90

$79.26

$117.52

high end

$35.92

$43.73

$57.78

$111.66

$147.58

B.  Cost to Maintain and Operate a Plant Each year, including for Fuel:  $/MWHr

Solar

Wind

Gas

Coal

Nuclear

low end

$4.00

$9.24

$24.38

$23.19

$23.87

mid-point

$4.66

$10.64

$26.51

$31.30

$25.11

high end

$5.33

$12.04

$28.64

$39.41

$26.35

C.  Short-term Variable Cost to Operate a Plant, including for Fuel:  $/MWHr

Solar

Wind

Gas

Coal

Nuclear

low end

$0.00

$0.00

$23.16

$14.69

$9.63

mid-point

$0.00

$0.00

$25.23

$18.54

$9.63

high end

$0.00

$0.00

$27.31

$22.40

$9.63

A number of points follow from these cost estimates:

a)  First, and as was discussed above, the LCOE estimates indicate that for the question of what new type of power plant to build, it will in general be cheapest to obtain new power from a solar or wind plant.  The mid-point LCOE estimates for solar and wind are well below the costs of power from gas plants, and especially below the costs from coal or nuclear plants.

But also as noted before, local conditions vary and there will in fact be a range of costs for different types of plants.  The Lazard estimates indicate that a gas plant with costs at the low end of a reasonable range (estimated to be about $32 per MWHr) would be competitive with solar or wind plants at the mid-point of their cost range (about $33 to $34 per MWHr), and below the costs of a solar plant at the high end of its cost range ($36) and especially a wind plant at its high end of its costs ($44).  However, there are not likely to be many such cases:  Gas plants with a cost at their mid-point estimate would not be competitive, and even less so for gas plants with a cost near their high end estimate.

Furthermore, even the lowest cost coal and nuclear plants would be far from competitive with solar or wind plants when considering the building of new generation capacity.  This is consistent with what we saw in Section D above, of no new coal or nuclear plants being built in recent years (with the exception of one nuclear plant whose construction started decades ago and was only finished in 2016).

b)  More interesting is the question of whether it is economic to build new solar or wind plants to substitute for existing gas, coal, or nuclear plants.  The figures in panel B of the table on the cost to operate and maintain a plant for another year (all in terms of $/MWHr) can give us a sense of whether this is worthwhile.  Keeping in mind that these are going to be low estimates (as they are the costs for newly built plants, using the technologies available today, not for existing ones which were built possibly many years ago), the figures suggest that it would make economic sense to build new solar and wind plants (at their LCOE costs) and decommission all but the most efficient coal burning plants.

However, the figures also suggest that this will not be the case for most of the existing gas or nuclear plants.  For such plants, with their capital costs already incurred, the cost to maintain and operate them for a further year is in the range of $24 to $29 (per MWHr) for gas plants and $24 to $26 for nuclear plants.  Even recognizing that these costs estimates will be low (as they are based on what the costs would be for a new plant, not existing ones), only the more efficient solar and wind plants would have an LCOE which is less.  But they are close, and are on the cusp of the point where it would be economic to build new solar and wind plants and decommission existing gas and nuclear plants, just as this is already the case for most coal plants.

c)  Panel C then provides figures to address the question of which power plants to operate, for those which are available for use on any given day.  With no short-term variable cost to generate power from solar or wind sources (they burn no fuel), it will always make sense to use those sources first when they are available.  The short-term cost to operate a nuclear power plant is also fairly low ($9.63 per MWHr in the Lazard estimates, with no significant variation in their estimates).  Unlike other plants, it is difficult to turn nuclear plants on and off, so such plants will generally be operated as baseload plants kept always on (other than for maintenance periods).

But it is interesting that, provided a coal burning plant was kept active and not decommissioned, the Lazard figures suggest that the next cheapest source of power (if one ignores the pollution costs) will be from burning coal.  The figures indicate coal plants are expensive to maintain (the difference between the figures in panel B and in panel C) but then cheap to run if they have been kept operational.  This would explain why we have seen many coal burning plants decommissioned in recent years (new solar and wind capacity is cheaper than the cost of keeping a coal burning plant maintained and operating), but that if the coal burning plant has been kept operational, that it will then typically be cheaper to run rather than a gas plant.

d)  Finally, existing gas plants will cost between $23 and $27 per MWHr to run, mostly for the cost of the gas itself.  Maintenance costs are low.  These figures are somewhat less than the cost of building new solar or wind capacity, although not by much.

But there is another consideration as well.  Suppose one needs to add to night-time capacity, so solar power will not be of use (assuming storage is not an economic option).  Assume also that wind is not an option for some reason (perhaps the particular locale).  The LCOE figures indicate that a new gas plant would then be the next best alternative.  But once this gas plant is built, it will be available also for use during the day.  The question then is whether it would be cheaper to run that gas plant during the day also, or to build solar capacity to provide the day-time power.

And the answer is that at these costs, which exclude the costs from the pollution generated, it would be cheaper to run the gas plant.  The LCOE costs for new solar power ranges from $31 to $36 per MWHr (panel A above), while the variable cost of operating a gas plant built to supply nighttime capacity ranges between $23 and $27 (panel C).  While the difference is not huge, it is still significant.

This may explain in part why new gas generation capacity is not only being built in the US, but also is then being used more than other sources for additional generation, even though new solar and wind capacity would be cheaper.  And part of the reason for this is that the costs imposed on others from the pollution generated by burning fossil fuels are not being borne by the power plant operators.  This will be examined in the next section below.

F.  The Impact of Including the Cost of Greenhouse Gas Emissions

Burning fossil fuels generates pollution.  Coal is especially polluting, in many different ways. But I will focus here on just one area of damage caused by the burning of fossil fuels, which is that from their generation of greenhouse gases.  These gases are warming the earth’s atmosphere, with this then leading to an increased frequency of extreme weather events, from floods and droughts to severe storms, and hurricanes of greater intensity.  While one cannot attribute any particular storm to the impact of a warmer planet, the increased frequency of such storms in recent decades is clearly a consequence of a warmer planet.  It is the same as the relationship of smoking to lung cancer.  While one cannot with certainty attribute a particular case of lung cancer to smoking (there are cases of lung cancer among people who do not smoke), it is well established that there is an increased likelihood and frequency of lung cancer among smokers.

When the costs from the damage created from greenhouse gases are not borne by the party responsible for the emissions, that party will ignore those costs.  In the case of power production, they do not take into account such costs in deciding whether to use clean sources (solar or wind) to generate the power needed, or to burn coal or gas.  But the costs are still there and are being imposed on others.  Hence economists have recommended that those responsible for such decisions face a price which reflects such costs.  A specific proposal, discussed in an earlier post on this blog, is to charge a tax of $40 per ton of CO2 emitted.  All the revenue collected by that tax would then be returned in equal per capita terms to the American population.  Applied to all sources of greenhouse gas emissions (not just power), the tax would lead to an annual rebate of almost $500 per person, or $2,000 for a family of four.  And since it is the rich who account most (in per person terms) for greenhouse gas emissions, it is estimated that such a tax and redistribution would lead to those in the lowest seven deciles of the population (the lowest 70%) receiving more on average than what they would pay (directly or indirectly), while only the richest 30% would end up paying more on a net basis.

Such a tax on greenhouse gas emissions would have an important effect on the decision of what sources of power to use when power is needed.  As noted in the section above, at current costs it is cheaper to use gas-fired generation, and even more so coal-fired generation, if those plants have been built and are available for operation, than it would cost to build new solar or wind plants to provide such power.  The costs are getting close to each other, but are not there yet.  If gas and coal burning plants do not need to worry about the costs imposed on others from the burning of their fuels, such plants may be kept in operation for some time.

A tax on the greenhouse gases emitted would change this calculus, even with all other costs as they are today.  One can calculate from figures presented in the Lazard report what the impact would be.  For the analysis here, I have looked at the impact of charging $20 per ton of CO2 emitted, $40 per ton of CO2, or $60 per ton of CO2.  Analyses of the social cost of CO2 emissions come up with a price of around $40 per ton, and my aim here was to examine a generous span around this cost.

Also entering is how much CO2 is emitted per MWHr of power produced.  Figures in the Lazard report (and elsewhere) put this at 0.51 tons of CO2 per MWHr for gas burning plants, and 0.92 tons of CO2 per MWHr for coal burning plants.  As has been commonly stated, the direct emissions of CO2 from gas burning plants is on the order of half of that from coal burning plants.

[Side note:  This does not take into account that a certain portion of natural gas leaks out directly into the air at some point in the process from when it is pulled from the ground, then transported via pipelines, and then fed into the final use (e.g. at a power plant).  While perhaps small as a percentage of all the gas consumed (the EPA estimates a leak rate of 1.4%, although others estimate it to be more), natural gas (which is primarily methane) is itself a highly potent greenhouse gas with an impact on atmospheric warming that is 34 times as great as the same weight of CO2 over a 100 year time horizon, and 86 times as great over a 20 year horizon.  If one takes such leakage into account (of even just 1.4%), and adds this warming impact to that of the CO2 that is produced by the gas that has not leaked out but is burned, natural gas turns out to have a similar if not greater atmospheric warming impact as that resulting from the burning of coal.  However, for the calculations below, I will leave out the impact from leakage.  Including this would lead to even stronger results.]

One then has:

D.  Cost of Greenhouse Gas Emissions:  $/MWhr

Solar

Wind

Gas

Coal

Nuclear

Tons of CO2 Emitted per MWHr

0.000

0.000

0.510

0.920

0.000

Cost at $20/ton CO2

$0.00

$0.00

$10.20

$18.40

$0.00

Cost at $40/ton CO2

$0.00

$0.00

$20.40

$36.80

$0.00

Cost at $60/ton CO2

$0.00

$0.00

$30.60

$55.20

$0.00

E.  Levelized Cost of Energy for a New Power Plant, including Cost of Greenhouse Gas Emissions (mid-point figures):  $/MWHr

Solar

Wind

Gas

Coal

Nuclear

Cost at $20/ton CO2

$33.58

$33.19

$55.10

$97.66

$117.52

Cost at $40/ton CO2

$33.58

$33.19

$65.30

$116.06

$117.52

Cost at $60/ton CO2

$33.58

$33.19

$75.50

$134.46

$117.52

F.  Short-term Variable Cost to Operate a Plant, including Fuel and Cost of Greenhouse Gas Emissions (mid-point figures):  $/MWHr

Solar

Wind

Gas

Coal

Nuclear

Cost at $20/ton CO2

$0.00

$0.00

$35.43

$36.94

$9.63

Cost at $40/ton CO2

$0.00

$0.00

$45.63

$55.34

$9.63

Cost at $60/ton CO2

$0.00

$0.00

$55.83

$73.74

$9.63

Panel D shows what would be paid, per MWHr, if greenhouse gas emissions were charged for at a rate of $20 per ton of CO2, of $40 per ton, or of $60 per ton.  The impact would be significant, ranging from $10 to $31 per MWHr for gas and $18 to $55 for coal.

If these costs are then included in the Levelized Cost of Energy figures (using the mid-point estimates for the LCOE), one gets the costs shown in Panel E.  The costs of new power generation capacity from solar or wind sources (as well as nuclear) are unchanged as they have no CO2 emissions.  But the full costs of new gas or coal fired generation capacity will now mean that such sources are even less competitive than before, as their costs now also reflect, in part, the damage done as a result of their greenhouse gas emissions.

But perhaps most interesting is the impact on the choice of whether to keep burning gas or coal in plants that have already been built and remain available for operation.  This is provided in Panel F, which shows the short-term variable cost (per MWHr) of power generated by the different sources.  These short-term costs were primarily the cost of the fuel used, but now also include the cost to compensate for the damage from the resulting greenhouse gas emissions.

If gas as well as coal had to pay for the damages caused by their greenhouse gas emissions, then even at a cost of just $20 per ton of CO2 emitted they would not be competitive with building new solar or wind plants (whose LCOEs, in Panel E, are less).  At a cost of $40 or $60 per ton of CO2 emitted, they would be far from competitive, with costs that are 40% to 120% higher.  There would be a strong incentive then to build new solar and wind plants to serve what they can (including just the day time markets), while existing gas plants (primarily) would in the near term be kept in reserve for service at night or at other times when solar and wind generation is not possible.

G.  Summary and Conclusion

The cost of new clean sources of power generation capacity, wind and especially solar, has plummeted over the last decade, and it is now cheaper to build new solar or wind capacity than to build new gas, coal, and especially nuclear capacity.  One sees this not only in estimates based on assessments of the underlying costs, but also in the actual market prices for new generation capacity (the PPA prices in such contracts).  Both have plummeted, and indeed at an identical pace.

While it was only relatively recently that the solar and wind generation costs have fallen below the cost of generation from gas, one does see these relative costs reflected in the new power generation capacity built in recent years.  Solar plus wind (together) account for the largest single source of new capacity, with gas also high.  And there have been no new coal plants since 2013 (nor nuclear, with the exception of one plant coming online which had been under construction for decades).

But while solar plus wind plants accounted for the largest share of new generation capacity in recent years, the impact on the overall mix was low.  And that is because not that much new generation capacity has been needed.  Up until to at least 2017, efficiency in energy use was improving to such an extent that no net new capacity was needed despite robust GDP growth.  A large share of something small will still be something small.

However, the costs of building new solar or wind generation capacity have now fallen to the point where it is cheaper to build new solar or wind capacity than it costs to maintain and keep in operation many of the existing coal burning power plants.  This is particularly the case for the older coal plants, with their older technologies and higher maintenance costs.  Thus one should see many of these older plants being decommissioned, and one does.

But it is still cheaper, when one ignores the cost of the damage done by the resulting pollution, to maintain and operate existing gas burning plants, than it would cost to build new solar or wind plants to generate the power they are able to provide.  And since some of the new gas burning plants being built may be needed to add to night-time generation capacity, this means that such plants will also be used to generate power by burning gas during the day, instead of installing solar capacity.

This cost advantage only holds, however, because gas-burning plants do not have to pay for the costs resulting from the damage their pollution causes.  While they pollute in many different ways, one is from the greenhouse gases they emit.  But if one charged them just $20 for every ton of CO2 released into the atmosphere when the gas is burned, the result would be different.  It would then be more cost competitive to build new solar or wind capacity to provide power whenever they can, and to save the gas burning plants for those times when such clean power is not possible.

There is therefore a strong case for charging such a fee.  However, many of those who had previously supported such an approach to address global warming have backed away in recent months, arguing that it would be politically impossible.  That assessment of the politics might be correct, but it really makes no sense.  First, it would be politically important that whatever revenues are generated are returned in full to the population, and on an equal per person basis.  While individual situations will of course vary (and those who lose out on a net basis, or perceive that they will, will complain the loudest), assessments based on current consumption patterns indicate that those in the lowest seven deciles of income (the lowest 70%) will on average come out ahead, while only those in the richest 30% will pay more.  It is the rich who, per person, account for the largest share of greenhouse gas emissions, creating costs that others are bearing.  And a redistribution from the richest 30% to the poorest 70% would be a positive redistribution.

But second, the alternative to reducing greenhouse gas emissions would need to be some approach based on top-down directives (central planning in essence), or a centrally directed system of subsidies that aims to offset the subsidies implicit in not requiring those burning fossil fuels to pay for the damages they cause, by subsidizing other sources of power even more.  Such approaches are not only complex and costly, but rarely work well in practice.  And they end up costing more than a fee-based system would.  The political argument being made in their favor ultimately rests on the assumption that by hiding the higher costs they can be made politically more acceptable.  But relying on deception is unlikely to be sustainable for long.

The sharp fall in costs for clean energy of the last decade has created an opportunity to switch our power supply to clean sources at little to no cost.  This would have been impossible just a few years ago.  It would be unfortunate in the extreme if we were to let this opportunity pass.