The Index Investor
August 2020

Our 12-month regime forecast probabilities have not changed over the past month. The probability of the Persistent Deflation Regime remains 65%; the High Uncertainty Regime 15%; the High Inflation Regime 15%, and the Normal Regime 5%.

Over the next twelve months, evidence continues to accumulate that the real economy is in worse shape that equity market investors apparently believe. COVID lab test bottlenecks continue in the US, and infection levels remain high. As cold weather returns to the northern hemisphere, COVID cases will likely increase, assuming more people congregate in enclosed areas with insufficient ventilation and other measures to limit the accumulation of coronavirus aerosols to a level that can trigger superspreader events.

Bankruptcies are increasing; in the case of small and medium enterprises, this is leading not to Chapter 11 restructurings, but Chapter 7 liquidations, which represent a permanent loss of jobs and productive capacity.

The continuing failure of the politically polarized US Congress to pass another stimulus plan will very likely accelerate these trends.

We have made no change to our 36-month regime forecasts. The probability the United States will be in the Persistent Deflation Regime remains 50%, as evidence continues to accumulate that the economy will very likely suffer a deeper and longer downturn than many investors currently expect.

It seems very likely that the US will experience a constitutional crisis after the November election. This will sharply increase uncertainty, with its attendant negative impact on the economy. At this point, it does not appear that this has been fully incorporated into asset market prices.

It also appears that the probability of an intensifying political crisis in the United States regardless of who is eventually sworn in as the nation’s next president has not been fully incorporated into the dominant narratives driving asset prices today.

The essence of the issue is this: Following the 2008 Great Financial Crisis, substantial numbers of middle class Americans lost their homes to foreclosure. Today, many of those same families are losing their jobs and having to close businesses in which they have invested all their savings and spent years building. And to make matters even worse, they are slowly beginning to realize that the COVID learning losses their children have experienced in school are very unlikely to be remediated, which has dire implications for their future prospects in the post-COVID economy (and the growth of the economy itself, as those losses will also depress future productivity gains). In the 2016 election we saw that the effects of the GFC unleashed social and political forces whose power elites failed to comprehend. I suspect that is happening again, with future effects that unknown at this point, but which will almost certainly lead to substantial increases in uncertainty.

Thus far, China’s response to COVID’s economic shock has followed its traditional stimulus playbook, with large, debt financed increases in spending on infrastructure, real estate, and by state owned enterprises. The problem is that every time they use it, the investments get even more marginal in terms of their potential economic return, and thus ability to repay their debt load. As Herbert Stein said, “if something cannot go on forever, it will stop.” And when it does, China will very likely face some type of crisis that could lead to a combination of increased internal disorder (less likely) and more aggressive external behavior (more likely).

Another important new development was Iran’s announcement that it is building a new port on the Gulf of Oman that will open in March 2021. This will enable it to export oil without having to ship it (to China) through the Strait of Hormuz. This will give it much more freedom to mine or otherwise attempt to close the Strait in the future. That may happen sooner than many expect, especially if Israel perceives that Donald Trump will not remain president, and that a Biden administration will be much less supportive of what the current Israel leadership appears to believe is an existential confrontation with Iran. This would likely incentivize Israel to speed up planned actions toward Iran if it believes its scope for taking those actions will soon be reduced.

Finally, it is important to keep in mind that the headwinds that were restraining aggregate demand growth before the devastating arrival of the COVID-19 pandemic either have not improved or are worsening. These include slower population growth and faster aging, weak productivity growth, declining labor share of GDP, rising levels of both inequality and debt, and the growing threat of job displacement as increasingly capable automation and artificial intelligence technologies are deployed.

Our 36-month forecast probability for being in the High Inflation Regime remains at 35%.

Whether the High Inflation Regime comes to pass will depend on the interaction between COVID19’s shocks to demand and supply, along with even more important political factors.

We know that the demand shock has been swift and severe, and has triggered falls in the US Consumer Price Index. Current evidence leads to the conclusion that depressed demand may last longer than many expect. The longer it takes to develop and deploy an effective vaccine, more layoffs will occur, more bankruptcies will take place, and the long term impact of uncertainty will be stronger. All of these depress consumption, investment, and export demand. If supply remained constant, this would tend to decrease rather than increase prices.

However, COVID19 has also been a shock to aggregate supply; many past episodes of high inflation were caused by such shocks (e.g., oil supply cutbacks in 1973 and 1979). In June, the US Consumer Price Index reversed its recent three months of decline, and increased by 0.6%. This was primarily driven by a 5.1% increase in energy prices, which reflected the interaction of a modest increase in demand as quarantines were lifted with a much larger reduction in supply.

On balance, we continue to believe that the impact of demand destruction will outweigh the impact of supply reduction.

In our view, the most logical cause of a return to the High Inflation regime over the next 36 months would be a crisis of confidence in the US government and/or economy that causes a flight from the US dollar and a sharp rise in the price of imported goods (and very likely gold as well).

Another potential cause of such a crisis would be the outbreak of open conflict between the China and the United States (e.g., a Chinese invasion of Taiwan or rapid escalation of a kinetic encounter in the South China Sea) in which American forces were defeated and/or global supply chains were severely disrupted.

Finally, at the 36-month time horizon, our forecast probabilities for the High Uncertainty (10%) and Normal Regimes (5%) also remain unchanged. The former regime could occur if the Democrats win the White House and both houses of Congress in November, and struggle to revive the economy with good policy initiatives, but without an effective coronavirus vaccine while facing intensifying conflict with China.

Return to the Normal Regime would require the same political wins and policy initiatives, avoidance of new uncertainty shocks, early deployment of a highly effective vaccine, and a sharp reduction in US-China tensions (e.g., due to the removal of Xi Jinping from office).

In reviewing the current situation, we are again reminded that when uncertainty is high, people rely more heavily on social learning and copying what others are doing. Not only does this slow the diffusion of new information throughout social systems like economies and financial markets, but it also causes these systems to coalesce around a small number of increasingly fragile narratives. Under these conditions, rapid, non-linear changes are very likely to occur.

That certainly appears to be the case today in global financial markets.

Portfolio Allocation Implications of Our Forecast

We take two approaches to deriving the tactical asset allocation implications from our analyses. The first takes a systematic approach, and is based on relative asset class valuations. Our starting point is our “neutral” model portfolio, which is equally weighted across nine broad asset classes, and also includes a 10% allocation to alpha strategies (equity market neutral and global macro) that are designed to have a low correlation to returns on broad asset classes. Based on asset class valuations, we systematically vary the asset class weights (but not the active strategy weight), increasing from 10% to 15% when an asset class is likely undervalued, and 15% when it is very likely undervalued. In the case of overvaluations, we go to 5% and then into cash, if there are no undervalued asset classes with room for an increase. In effect, this replicates the systematic rebalancing strategy we used for 15 years in our previous model portfolios.Based on subscriber requests, this month we are re-introducing a feature from the previous version of The Index Investor: Tactical Asset Allocation Implications from our analyses.

The second tactical approach is based on our subjective view not only of current asset class valuations, but also of the implications of the broader macro trends and uncertainties that we analyze each month. Importantly, this subjective view reflects our primary goal of avoiding large downside losses, rather than seeking large upside gains.

Two final notes: First, with respect to US fixed income, we include credit products (investment grade and high yield) in the same asset class as government debt, and will shift into the former when their valuations become attractive. Second, we regard gold not as a separate asset class to be held long-term, but rather as a complement to cash, into which we shift in periods of substantial overvaluation across multiple asset classes.

More information about our investment beliefs, including our core philosophy, approach to asset allocation (including our model portfolios and their long-term track record), and views on various approaches to active and passive management can all be found here.

Here is our latest asset allocation view:

Implications of the Most Recent Three Month Asset Class Returns

Our quantitative forecast methodology focuses on the level and change in three-month returns, over the most recent and previous three-month periods, for those asset classes, which should perform best under different regimes (in this sense, our regimes can be regarded as macro factors). We assume that relatively higher returns are associated with more widely held investor belief in the probability that a given macro regime will develop in the future.

Note: The language we use to describe our estimated likelihood of asset class over or undervaluation is based on US Intelligence Community Directive 203 on Analytic Standards, which includes the following table:

Market Stress Indicators (@31Jul20)

As always, this month’s Evidence File contains important new indicators and surprises observed over the past month in addition to those cited in our forecast update. Overall, these new pieces of evidence indicate that a substantial increase in uncertainty very likely lies ahead, that is not yet reflected either in dominant popular narratives or financial market prices. Key indicators include:

• New analyses from both McKinsey and Deloitte found that companies are shifting their technology investments towards areas that will likely lead to job losses; however progress is being restrained by a lack of talent.

• This ties into the low likelihood that COVID learning losses are not going to be remediated, absent very substantial (and very unlikely) changes to the United States’ K-12 education system. This means that the talent shortage that is restraining companies’ investment in advanced technologies will very likely worsen. Not only will this hold down economy wide productivity gains (and thus the economic growth that is critical to serving the exponential growth in debt that COVID had triggered), but it will also support continuation of “winner take all” dynamics in many industries. Companies that can attract scarce talent can maximize the use of advanced technologies, which leads to higher market share, bigger profit margins, and the ability to pay talent more than competitors. The result is both worsening income inequality and increased concentration in a growing number of industries.

• New research was published on the aerosol transmission of coronavirus, and the importance of indoor air quality, masks, and physical distancing to limit superspreader events when more people head inside when the seasons change and temperatures drop in the northern hemisphere. On the bright side, new reports also found that population heterogeneity in individual responses to coronavirus means that we could be closer to limited herd immunity than previously estimated.

• The nations of the European Union finally agreed on a 750 billion Euro stimulus package. Along with much lower COVID infection rates than in the US, this should lead to a quickening economic recovery.

• The latest PEW Research poll finds that a record high 73% of Americans have an unfavorable opinion of China (68% of Democrats, and 83% of Republicans), up from just 29% in 2006. If Biden wins in November, US conflict with China is unlikely to moderate, unless and until Xi Jinping is removed as the nation’s leader.

• A series of new reports provided further evidence that America’s level of social capital has continued to decline, and the multiple negative effects that has had, not the least on the nation’s politics.

• In the US, multiple analyses focused on the fraught relationship between the increasingly progressive Democratic Party and the white working class. Whether Biden can win back voters who supported Obama in 2008 and 2012, but Trump in 2016 could be critical to his winning the swing states that cost Hilary Clinton the last election.

Pre-Mortem Analysis

One of the most important forecasting disciplines is to ask yourself why your forecast could be wrong. Dr. Gary Klein’s research has shown that a very powerful and insightful way to do this is via a “pre-mortem analysis.” This method asks you to assume that it is a point in the future, and your forecast has been proven wrong (or your strategy or company has failed). You are then asked to look backward from this imagined point in the future, to explain why you failed, what you missed, and what you could have done differently to avoid your fate.

The pre-mortem method takes advantage of the fact that humans reason much more concretely and in more detail when explaining the past than they do when trying to forecast the future.

So let us assume that it is one year from now, and our current forecast has turned out to be wrong.

How did this happen? What developments did we fail to anticipate?

• A number of world leaders – Xi Jinping and Donald Trump, and to a lesser extent Vladimir Putin and Ali Khamenei are all facing sharply weakened economies and declining political popularity. History teaches us that this can lead to increased “foreign adventurism” to distract the public from worsening domestic conditions, as a nation rallies around its leader in a period of heightened external conflict. Should a “kinetic” conflict develop between China and the United States, or Iran and Israel, Saudi Arabia, and/ or the US, or between Russia and one or more European countries (e.g., due to a Russian incursion into the Baltics), it would generate a very sharp increase in uncertainty that would likely accelerate the already sharp COVID-19 economic slowdown. Given given high debt levels, this would very likely accelerate the arrival of the Persistent Deflation Regime.

• On the other hand, the removal from office (by one means or another) of Xi Jinping or Donald Trump * could * lead to a reduction in the dangerously growing conflict between the two nations, and increase cooperation in both the fight against COVID19 and the recovery of the global economy. This would somewhat reduce the probability of both the Persistent Deflation and High Inflation Regimes, and raise the relative probabilities of the High Uncertainty and Normal Times Regimes.

• A supply side shock of some type – beyond the disruption of global supply chains caused by COVID-19 -- could produce a sudden increase in inflation. The most likely scenario is a reduction in oil supplies due to a prolonged kinetic conflict between Iran and the US. An unlikely scenario could be major crop failures associated with the next solar cycle, which NASA forecasts will be the weakest in 200 years. McKinsey recently concluded that the probability of such a failure has increased due to changes in the environment, and now stands at about 10% over the next five years.

• Another route to the high inflation regime (repeatedly noted by Bridgewater’s Ray Dalio) would be a sudden loss of confidence in the US dollar relative to other currencies (driving up import prices), perhaps because of increasing deficit monetization and policy paralysis if a severe downturn continues without meaningful policy reforms to address critical structural challenges. However, a sharp rise in import prices due to a collapse in the USD exchange rate also requires relatively higher confidence in another currency, with the Euro being the most likely candidate. This currently seems unlikely, given both the Eurozone’s economic and political uncertainty, and the prospect of an intensifying conflict between the West and China. If confidence collapsed in all major currencies, the price of gold would rise, and price inflation would only occur in terms of gold.

Note: Combining Our Forecasts with Others From Other Sources and Extremizing the Result Should Increase Your Predictive Accuracy

Research has found that three steps can improve forecast accuracy. The first is seeking forecasts based on different forecasting methodologies, or prepared by forecasters with significantly different backgrounds (as a proxy for different mental models and information). The second is combining those forecasts (using a simple average if few are included, or the median if many are). The final step, which significantly improved the performance of the Good Judgment Project team in the IARPA forecasting tournament, is to “extremize” the average (mean) or median forecast by moving it closer to 0% or 100%.

Forecasts for binary events (e.g., the probability an event will or will not happen within a given time frame) are most useful to decision makers when they are closer to 0% or 100% than the uninformative “coin toss” 50%. As described by Baron et al in “Two Reasons to Make Aggregated Probability Forecasts More Extreme”, forecasters will often shrink their probability estimates towards 50% to take into account their subjective belief about the extent of potentially useful information that they are missing.

When you average multiple forecasters’ estimates, you are including more information, which should increase forecast confidence and push the mean estimate closer to 0% or 100%. However, this doesn’t happen when you use simple averaging. For this reason, forecast accuracy is increased when you employ a structured “extremizing” technique to move the mean estimate closer to 0% or 100%.

You can download an extremizing model from our website to use when combining the forecasts you use in your decision process.

The extremizing factors in our model are those that the Good Judgment Project found maximized the accuracy of combined forecasts. Note that the extremizing factor is lower when average forecaster expertise is higher. This is based on the assumption that a group of expert forecasters will incorporate more of the full amount of potentially useful information than will novice forecasters.

Feature Article: Joe Biden Wants to Remove Carbon From US Electricity Generation by 2035. Is That Realistic?

In 2019, the United States consumed 4,118,000 gigawatt hours of electricity (4,118 billion kilowatt hours, or 4,118 terawatt hours).

By generation fuel, 63% was produced using fossil fuels (24% coal, 38% natural gas, 1% other). Nuclear accounted for 20%, and Renewables the remaining 17% (7% wind, 7% hydro, 2% solar, 1% other).

Realizing the Biden plan’s goals will require some combination of four changes:

1) A large increase in nuclear generation (very unlikely);

2) A large increase in the use of carbon capture and storage technologies (which, given current technologies, would produce a substantial increase in electricity prices);

3) A very large increase in generation from wind and solar; and

4) A very substantial increase in the use of utility scale battery storage technologies (both to reduce the use of gas fired plants that run to meet demand peaks, and to support the expansion of intermittent generation technologies like wind and solar).

The expansion of utility scale battery storage is by far the most uncertain of these options, yet seems to be the critical assumption that underlies the Biden plan. Given its disruptive potential, it is important to understand the underlying issues.

Let’s start by clarifying the confusing terminology used battery storage discussions. Megawatts are a measure of the power rating of a battery – how fast it can discharge stored energy. The more power that can be released over a given period of time, the more powerful the battery.

In contrast, Megawatt Hours are a measure of how much energy is storied in the battery – how much energy it can discharge.

Mathematically, a battery’s Megawatt Hours equal its power times the length of time (in hours) it can discharge its full power.

These highlight a fundamental battery tradeoff: You can get full power for short period of time, or a lower amount of power for a longer period of time. For example, a 60MW battery that stores 240 MWh of energy can discharge 60MW for four hours, or 30MW for eight hours.

Here’s an example. There are basically two types of power generation units. “Baseload” units have a high rate of capacity utilization, while “Peaking” units tend to be older and less efficient, and only generate power when daily electricity demand is at its highest point (note that these peak periods are of varying lengths, depending on the season and location of demand – e.g., areas with large cities tend to have longer peaks).

In the United States in 2018, almost 90% of electricity generated from natural gas was produced by highly efficient combined cycle baseload plants, while only 10% was produced by less efficient (and often ore polluting) combustion turbine peaker plants.

According to a recent study, New York’s gas fired peaker plants have a capacity of 4,500 Megawatts (4.5 gigawatts). The study’s authors concluded that, if cost efficient batteries that could discharge for 6 hours were installed, they could replace 275 Megawatts of this gas fired peaker capacity. Batteries that could discharge for 8 hours could replace 500 MW of gas fired peaker capacity. And if the 6-hour batteries were paired with new solar generation facilities, 1,804 MW of gas peaker capacity could be displaced.

That sounds great, doesn’t it? That’s certainly the kind of story you see a lot these days, extolling the brave new (greener) world of renewables (especially solar) paired with batteries that is just around the corner.

But how real is this vision?

Let’s look at six issues that you don’t read about as much: (1) Grid control; (2) Profitability of the battery storage revenue model; (3) Batteries’ cost; (4) Batteries’ long term safety and reliability; (5) Batteries’ use of rare earth minerals; and (6) How climate change will affect future solar radiation and wind.

Grid Control

Buried in the New York study was this critical sentence: “We did not consider [battery] charging constraints if multiple gas fired peakers were removed” and replaced with solar generation. The underlying assumption of pairing batteries with solar generation is that there will be times during the day when all the generating facilities on a grid will be producing more electricity than is required to satisfy total demand. During those periods, it is assumed that solar facilities will be allowed by the system operator to recharge their paired battery.

But by regulation, system operators are generally required to “dispatch” (i.e., take power from) producers in order of their cost, with the lowest cost facilities (e.g., nuclear plants) being dispatched first, and the most expensive ones (e.g., gas peakers) dispatched last. What happens if solar is so cheap that is frequently dispatched and has insufficient time to recharge the battery that is supposed to meet peak power demands (e.g., late on a winter afternoon when the sun has gone down and people come home and turn on a lot of electrical appliances)?

While it is not yet clear how regulators will make this tradeoff, it seems very likely that it will end up being litigated.

A closely related issue is the minimum number of hours that regulators and system operators will require battery storage operators to be able to provide. For example, a four hour minimum is likely to result in many more battery storage facilities than an eight hour requirement (which is why the PJM system operator has recently set an 11 hour requirement for storage operators to have grid access – a decision which is also getting challenged: see, “Capacity Value of Energy Storage in PJM” by Astrape Consulting for the US Energy Storage Association and the Natural Resources Defense Council).

More broadly, as you add more intermittent (or variable renewable) generating sources like wind and power to an electricity grid, along with fast responding batteries in multiple locations, maintaining control of that grid becomes much more challenging.

In essence, grid control involves the simultaneous solution of four problems:

1) Power generation and transmission capacity must be sufficient to meet peak demand for electricity

2) Power systems must have adequate flexibility to address variability and uncertainty in demand (load) and generation resources

3) Power systems must be able to maintain steady frequency

4) Power systems must be able to maintain voltage within an acceptable range.

Multiple “smart grid” projects seek to meet these challenges; however there are still obstacles they must overcome to achieve their goals (e.g., “The Role of Smart Grids in Integrating Renewable Energy” by the US National Renewable Energy Laboratory, and “Smart Grid: Status and Outlook” by the Congressional Research Service).

Profitability of the Battery Storage Revenue Model

Perhaps the most famous battery storage facility in the world is The Hornsdale Power Reserve in South Australia, more commonly known as the Big Tesla Battery.

Built in 2017, it can (before a planned expansion) deliver 129 Megawatt Hours of energy for about one hour.

70MW of its 100MW power is used for maintenance of frequency and voltage control (i.e., “grid stability”, or “Frequency Control and Ancilliary Services”, or FCAS).

30MW is used for “load shifting” – storing excess electricity generated by the adjacent wind farm, and sold for short periods into the market for a higher price during peak demand periods, in competition with gas peakers and other battery storage facilities.

Initially, provision of FCAS services was very profitable; however, as more battery storage facilities have opened competition has increased.

Moreover, so called “park spreads” from storing and reselling energy have been thin, due to both “round trip” efficiency losses (only about 80% of the originally generated energy can be resold to meet peak demand), and margin pressure due to competition from other peak suppliers (e.g., other battery storage operators and gas generators with excess capacity they can ramp up).

On the bright side, a recent review by the US Energy Information Administration (“Battery Storage in the United States: An Update on Market Trends”) identified a larger number of potential sources of revenue for battery storage operators:

• Frequency regulation helps balance momentary differences between electricity demand and supply within the transmission grid, often in order to help maintain interconnection frequencies close to 60 Hertz.

• Spinning reserve is the unused dispatchable generating capacity of online assets that provides grid frequency management, which may be available to use during a significant frequency disturbance, such as during an unexpected loss of generation capacity. This reserve ensures system operation and availability. Dispatchable generators are those that can be turned on or off in order to meet immediate needs of the system.

• Voltage or reactive power support ensures the quality of power delivered by maintaining the local voltage within specified limits by serving as a source or sink of reactive power (the portion of electricity that establishes and sustains the electric and magnetic fields of alternating‐current equipment).

• Load following supplies (discharges) or absorbs (charges) power to compensate for load variations—this application is a power balancing application, also known as a form of ramp rate control.

• System peak shaving reduces or defers the need to build new central generation capacity or purchase capacity in the wholesale electricity market, often during times of peak demand.

• Arbitrage occurs when batteries charge during periods when electrical energy is less expensive and discharge when prices for electricity are high, also referred to as electrical energy time‐shift.

• Load management provides a demand side customer‐related service, such as power quality, power reliability (grid‐connected or microgrid operation), retail electrical energy time‐shift, demand charge management, or renewable power consumption maximization (charging the battery storage system during periods when renewable energy is greatest so as to consume the maximum renewable energy from the battery system, i.e. charging with solar during the day or charging with wind during high wind periods).

• Storing excess wind and solar generation reduces the rate of change of the power output from a non‐dispatchable generator in order to comply with local grid requirements related to grid stability or prevent over production or over‐production penalties. Non-dispatchable generators cannot be turned on or off in order to meet immediate needs and are often intermittent resources (generators with output controlled by the natural variability of the energy source, for example wind and solar).

• Backup power, following a catastrophic failure of a grid, provides an active reserve of power and energy that can be used to energize transmission and distribution lines, provides start‐up power for generators, or provides a reference frequency.

• Transmission and distribution deferral keeps the loading of the transmission or distribution system equipment below a specified maximum. This application allows for delays in transmission upgrades, avoids the need to upgrade a transmission system completely, or avoids congestion‐related costs and charges.

• Co‐located generator firming provides constant output power over a certain period of time of a combined generator and energy storage system. Often the generator in this case is a nondispatchable renewable generator (for example, wind or solar).

Batteries’ Cost

The cost of battery storage has declined in recent years. Most recently, Lazard and Company estimated that the levelized cost of storage (LCOS, which includes associated costs beyond the battery itself) for a 100 MW battery that could delivery this power for four hours (i.e., that could deliver 400 Megawatt Hours of energy) is now between $189 and $325 per Megawatt Hour, without subsidies.

In comparison, a new combined cycle gas generating plant costs between $44 and $68 per Megawatt Hour.

In “Cost Projections for Utility-Scale Battery Storage”, NREL most optimistic case estimates that by 2030 the cost for a four-hour system could fall to $124 per Megawatt Hour ($207 middle case), and $76 per Megawatt Hour ($156 middle case) by 2050.

To put this in perspective, in March this year, Florian Mayr published an analysis of a recent solar plus storage deal in Arizona (“Battery storage at US$20/MWh? Breaking down low-cost solar-plus-storage PPAs in the USA”). After a masterful piece of financial analysis, he concluded that behind the deal’s complicated terms, the estimated cost of the storage was about $310, which he noted was “within the rage of aggressive, but realistic quotes we observe in the industry today.”

But that is in a state that has optimal conditions for solar plus storage installations. It is clear that the levelized cost of storage is going to have to come down much more to support more widespread deployment of this model.

Another aspect of the cost issue was highlighted in the aforementioned EIA report: “There are two major challenges in determining the profitability and cost of battery storage systems.

“First, quantifying the competitiveness of a battery storage technology with other technologies operating on the grid must consider the individual markets that the storage technology is planning to be used in and what revenue opportunities exist for the technology.

“The second challenge involves the degradation of the system over time, which is the lasting and continuous decrease in either a battery’s power or energy performance or both and is linked to use or age of a battery component or system.

“The performance can be characterized by the full cycle power input and output at an agreed‐upon charge/discharge rate. There are two general options that can be employed to ensure reliable performance during a storage system’s lifetime:

(1) Overbuilding: adding more storage or discharge capacity behind the inverter than is needed, so that as the system ages it will maintain a capacity at or above the contracted capacity required of the system.

(2) Continual Upgrades: replacing some portion of the storage system to maintain the agreed‐upon performance during its lifetime.

“The two approaches to meeting performance requirements affect the installed capital costs of the system. Overbuilding storage capacity leads to a higher initial installed capital cost, while continual upgrades lead to higher operation and maintenance costs throughout the lifetime of the storage facility. Therefore, comparing only the normalized capital cost of various battery systems, [as we have done in this analysis] does not capture the variation in the lifetime costs.”

Batteries’ Long Term Safety and Reliability

In “Why Is the Utility Industry Less Bullish on Grid-Scale Storage?”, Kavya Balaraman observed that, “In Utility Dive's 2020 State of the Electric Utility survey, 27% of participants said they expect their organization will significantly increase grid-scale battery storage in the next 10 years — a significant reduction from 37% in 2018, and 34% in 2019.”

Balaraman noted that, “Matthew Raiford, manager of the Consortium for Battery Innovation, a research organization focused on lead batteries, said that ‘there’s also the looming issue of safety — the last couple of years have witnessed some high-profile safety-related issues with battery storage, including an explosion at an Arizona Public Service facility last April and multiple storage-related fires in South Korea…I would venture to guess that over the next few years, there will be a refocusing in the market, looking at things like safety, reliability and the kind of technical economics of utilizing these systems’”.

Raiford’s quote is familiar to anyone who has tried to sell a new technology to a utility company. They are, quite rightly, very conservative customers. They know all-too-well that regulators and customers are risk averse, and that they face significant downside costs for self-inflicted errors. Despite the enthusiasm of battery storage supporters, don’t expect utilities and their regulators to jump n the bandwagon any time soon.

Batteries’ Use Of Rare Earth Minerals

Lithium-ion batteries are the most widely used technology in grid-scale storage today. However, their manufacture relies on continuing supplies of reasonably priced lithium, cobalt, and a number of rare-earth minerals.

In light of growing US-China tensions, the continuing supply of the rare-earths cannot be guaranteed. Moreover, supplies of both lithium and cobalt are highly concentrated in a small number of countries (e.g., the Democratic Republic of the Congo for cobalt).

A recent report by UNCTAD highlighted the risks this poses to the world battery industry (“Commodities at a Glance: Special Issue on Strategic Battery Raw Materials”).

How Climate Change Will Affect Future Solar Radiation And Wind

Another critical assumption that underlies Joe Biden’s green power vision is that climate change will not have a negative effect on current levels of solar radiation and wind.

There is evidence that this assumption is more uncertain that many people would acknowledge. For example, in the Northeast United States, since the 1950s available solar energy in the summer has decreased, as the warming Great Lakes have produced more clouds (see, “Examining the Climatology of Shortwave Radiation in the Northeast United States”, by Hanrahan et al).

And in “Southward Shift Of The Global Wind Energy Resource Under High Carbon Dioxide Emissions”, Karnauskas et al find that, “the use of wind energy resource is an integral part of many nations’ strategies towards realizing the carbon emissions reduction targets set forth in the Paris Agreement, and global installed wind power cumulative capacity has grown on average by 22% per year since 2006.

“However, assessments of wind energy resource are usually based on today’s climate, rather than taking into account that anthropogenic greenhouse gas emissions continue to modify the global atmospheric circulation.” The authors “apply an industry wind turbine power curve to simulations of high and low future emissions scenarios in an ensemble of ten fully coupled global climate models to investigate large-scale changes in wind power across the globe.”

Their “calculations reveal decreases in wind power across the Northern Hemisphere mid-latitudes and increases across the tropics and Southern Hemisphere, with substantial regional variations. The changes across the northern mid-latitudes are robust responses over time in both emissions scenarios, whereas the Southern Hemisphere changes appear critically sensitive to each individual emissions scenario…Established features of climate change can explain these patterns: polar amplification is implicated in the northern mid-latitude decrease in wind power, and enhanced land–sea thermal gradients account for the tropical and southern subtropical increases.”

Conclusion

Substantial greenhouse gas emissions reductions via widespread deployment of wind and solar power, battery storage, and the Smart Grid is a seductive vision that may very well win Joe Biden votes in this November’s election.

As a practical matter, however, it is based on six critical assumptions that are far more uncertain than its supporters acknowledge.

Even if the probability for each of them having their most optimistic outcome is 90% (a very optimistic estimate), their joint probability is only 53% (90% to the 6th power). If the individual probabilities fall to 80%, the joint probability declines to just 26%.

To answer the question we started with, no, Joe Biden’s goal of removing carbon from US electricity generation by 2035 is not realistic.
 

High Value Information Observed In July 2020

In our model of the complex global macro system, change drivers are arrayed across a roughly chronological process (albeit one with many feedback loops), in which technological and environmental changes precede changes in the economy and national security, which in turn lead to changes in society and politics, all of which produce the effects we observe in investor behavior and financial market valuations and returns.


In our methodology, we classify new information as significant and highly valuable if either it (1) is an “indicator”, which reduces our uncertainty about the value of a parameter in our mental model for making sense of the dynamic macro system, or (2) it is a “surprise” which increases our uncertainty about either the range of potential values for a parameter or the structure of our model. With respect to indicators, the higher our priori probability is for a regime, the more we look for indicators that it will not occur, and the lower our prior probability for a regime, the more we look for indicators that it will occur. Put differently, we tend to look for high value indicators that disconfirm our prior views.

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System Tipping Points/Critical Threshold Analysis

Like Professors Andrew Lo, Doyne Farmer and others, we regard financial markets as a complex adaptive system (CAS), that exist as part of a larger macro system comprised of other CAS between which there are multiple feedback loops. These other systems include those that produce technology innovations, and economic, environmental, national security (including cyber), social, demographic, and political outcomes.

We also find that these systems tend to operate and generate effects in a rough chronological sequence, albeit with many feedback loops between them. The following chart highlights that the changes we observe in different areas at any point in time are actually part of a much more complex evolutionary process.

While most media coverage of these systems focused on flows (e.g., the size of the government deficit), rapid non-linear change in complex adaptive systems is often caused by a key stock (e.g., the amount of outstanding government debt) exceeding a critical threshold.

The next table highlights the key macro system stocks that we monitor.

In the next section, we will discuss information received over the past month that is related to these stocks, and which we believe is significant to our assessment of the probabilities that a critical threshold will be reached and a regime change will occur. We will conclude with our estimate, at the end of this month, of how close the macro system is to these critical thresholds, and the implications for financial market regime change probabilities.

How Close is the Macro System to One or More Critical Thresholds?

As we have noted, the macro drivers of financial market regime changes typically follow a rough chronological sequence, from technology to economic, security, social, and political causes and effects. Yet there are many feedbacks loops between them, creating complex root causes for many of the critical thresholds we have identified.

Understanding the time dynamics in this complex system is critical to avoiding substantial downside investment risk.

We use the UK Met Office Warning Model to communicate our assessment of these time dynamics. We estimate the time remaining before a critical macro system threshold is reached that could trigger a regime change, which is usually accompanied by substantial changes in asset class valuations.

The model uses three increasingly serious levels of warning, from “Be Aware” (condition yellow), to “Be Prepared” (condition orange), to “Take Action” (condition red).

For our purposes, we denote as “Be Aware” (yellow) critical thresholds that we assess to be three or more years away. We estimate that “Be Prepared” (orange) thresholds could be reached within 1 to 3 years. “Take Action” thresholds are very likely to be reached within one year.

Given their nature, we also note that in our three “wildcard” areas (Environment and Energy related; Disease and Human Caused Bioevents; and Cyber and Electromagnetic Events), our forecasts have higher levels of uncertainty.

The following charts summarize our current estimate of the time remaining before different critical thresholds will be reached.

Conclusion

At the highest level, we believe the complex adaptive global macro system can be in one of four states, based on its degree of order versus disorder, and degree of social cooperation versus conflict. A very coarse-grained reading of history suggests that these states evolve in a predictable cycle, from ordered/cooperative, to disordered/cooperative, to disordered/conflicted, to ordered/conflicted.

We believe that the system is currently in its most uncertain state, characterized by high degrees of underlying disorder and social conflict, both domestically and internationally. Beyond some point, intensifying conflict eventually increases the degree of order in the system. That appears to be happening now, via the increasing conflict between China, Russia, and Iran and the United States and other Western nations.