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BNEF's Latest "Embarrassingly" Lowball EV Outlook | CleanTechnica

This is a pretty solid critique of BNEF's EV forecasting.
Yours truly has been engaging BNEF people on Twitter. They seem to be fairly thin skinned about criticism.

One very important clarification from the CleanTechnica article is that BMI’s 1 TWh figure for 2022/2023 is not a forecast, but the current production pipeline. Their forecast would be at least 20% higher because more production not currently in the pipeline will come online by then. You guys are the experts but that seems to move peak oil even closer.

As BMI explained:

“[As of May 2019] We have battery capacity surpassing 1TWh by 2022/2023. In 2025 we have this at 1.35TWh… This isn’t a forecast however. This is an assessment of what is in the announced pipeline. More is coming, especially in North America, so our forecast for capacity would be 20% higher at least.” (Simon Moores, Benchmark)​

This tilts Benchmark’s current 2025 expectations towards 1.62 TWh and potentially higher (and still with room to grow between now and 2025). Although, details of announced plans on the US side will take time to materialise and get counted in Benchmark’s official figures.
 
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One very important clarification from the CleanTechnica article is that BMI’s 1 TWh figure for 2022/2023 is not a forecast, but the current production pipeline. Their forecast would be at least 20% higher because more production not currently in the pipeline will come online by then. You guys are the experts but that seems to move peak oil even closer.

As BMI explained:

“[As of May 2019] We have battery capacity surpassing 1TWh by 2022/2023. In 2025 we have this at 1.35TWh… This isn’t a forecast however. This is an assessment of what is in the announced pipeline. More is coming, especially in North America, so our forecast for capacity would be 20% higher at least.” (Simon Moores, Benchmark)​

This tilts Benchmark’s current 2025 expectations towards 1.62 TWh and potentially higher (and still with room to grow between now and 2025). Although, details of announced plans on the US side will take time to materialise and get counted in Benchmark’s official figures.
Yes, this is a very helpful clarification. It also shows that BMI understands that an industry can grow faster than what is explicitly in the pipeline.

Sadly, BNEF is bent on finding rationalizations for dialing back their EV forecast.

Cox Automotive Industry Update Report: May 2019 - Cox Automotive Inc.
This is another nice source on auto markets. Cox is estimating that used car sales are up 3.0% in US to 39.3m annualized. Meanwhile new cars are down 1.7% to 17.2m. This also implies that new vehicle sales are about 30% of all vehicles sales.

Why is this important? BNEF imagines that in some areas people with garages where they can charge may be only 30% of the market. Ok, first, that is silly because already this charging problem is being solved and by 2030s when BNEF says it will impede EV uptake, it will be even more solved. So the urban charging problem really is a red herring.

Nevertheless, there is yet another way to argue around this. Let's go ahead an stipulate that 30% of the world 2B fleet are an easy-to-replace-with-EV market. That's demand for 600M replacement vehicles. The other 1400M vehicles are in less-easy-to-replace markets. (We're just drawing a silly line here, but stay with me. EVs should have no inherent difficulty in thoroughly dominating the easy-to-replace market pushing to 100% of new vehicle sales in this segment. (I'm talking about over the next ten years or so.) What happens to the trade-in ICE that are getting replaced with EVs? Well they have the potential to migrate over to the less-easy markets. These markets place some durable value on ICE, but a lower cost used ICE can largely satisfy this market until ther is more than some 600M EVs on the road. (This will take a decade or more.) So basically all the easy markets can buy new EVs studying the 30% of the auto market needing a new vehicle, while all the used ICE flows into the 70% less-easy market. Now this is just a mathematical extreme to that the new auto market can go to 100% EV even when only 30% of the market is easy for EV. In reality, the math is easier because surely some less-easy market is quite willing and able to go EV. So it's alot easier for the new auto market to go all in on EVs.

The fatal mistake that BNEF is making in their argument is to ignore how the really huge problem of what to do with an abundance of ICE vehicles. As used ICE drops in value, it kills off demand for new ICE and it satisfies virtually any need for ICE vehicles. So just becaus only 600M of the fleet may be easy to replace with EVs does not mean that new auto sales are stuck at only 30% EVs. I suspect this is the sort of confusion of stock and flow that BNEF may be making in their model. The key thing is replacing the easy 600M first, and by that time lots of other issues will have been resolved. Simply put, the addressable market will expand faster than the satisfied market. I think the market will be hungry to find buyers for used ICE for quite a long time. And this, more than EV sales, is what drives down new ICE sales.
 
There's grassroots resistance against coal

"Grandma Ca: the 99-year-old standing up to Vietnam's coal rush
Toothless and nearly blind, grandmother Pham Thi Ca refuses to leave her plot of land even after bulldozers demolished her house -- an extraordinary holdout against communist Vietnam's deepening addiction to coal.[...]
Foreign investment has skewed Vietnam's energy strategy, locking it "into expensive and dirty power for decades," warns Julien Vincent, executive director at Market Forces, a non-governmental energy investment watchdog.[...]"

Grandma Ca: the 99-year-old standing up to Vietnam's coal rush
 
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U.S. Shale Oil Production Set To Grow 16% This Year | OilPrice.com

Rystad is predicting US shale oil adds 1.1 to 1.2 mb/d this year.

Yup, that pretty much covers global demand growth right there. While we've got plenty of reasons to question how long shale can keep this up, it's still quite an achievement that they keep doing this. WTI under $50/b seemed to slow their pace a bit, but they seem quite willing and able to oversupply the market for $60/b.
 
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Even IEA projections have been trimmed to 1.2M growth this year, which means we'll likely land at 1M or even less when all is pumped and done. Crazy story.

I'm strarting to think this plus trade war crashes the whole global economy and we see global peak demand next year or 2021. Seems rational.
 
Crude stockpiles up another 5M last week in the US.

If I'm a bigtime leveraged fracker in the US.....haven't I sold almost all of my production for the next two or three years already? Wouldn't banks insist on these guys "pre-selling" and ever increasing proportion of production via binding futures contracts as they become more and more leveraged?

This all sounds very bad. Am I missing something?
 
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Crude stockpiles up another 5M last week in the US.

If I'm a bigtime leveraged fracker in the US.....haven't I sold almost all of my production for the next two or three years already? Wouldn't banks insist on these guys "pre-selling" and ever increasing proportion of production via binding futures contracts as they become more and more leveraged?

This all sounds very bad. Am I missing something?

Its not very bad, yes there are exceptions, it actually quite resilient.

production would only need to be forward sold to cover cost of business, so perhaps 12 months, any excess production becomes simple profit.

the risk is not with either the bank or the fracker, the risk/reward is with the fracker's investors and the customer who prebrought the production.

other major commodities were/are sold on a 1year production basis, oil has always wanted to be special and have a significant spot price, but thats more the exception than the norm. do you really think Japanese battery producers pay spot price for iron, copper, nickel and cobalt?
 
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@
PEV Logistic.png
PEV Log Logistic.png


In an effort to make a simple, but compelling forecast of plug-in EV market share, I have boiled things down to a random walk on a logit scale. May sound complex, but it actually makes very few assumptions. I examine market share from 2012 to 2018, computing the annual increase in logit market share (log(S/(1-S)). So I've got now 6 data points. I compute the mean and sample standard deviations. I make the assumption that future logit differences have the same distribution as was operative in the past. Thus, the future is modeled as a random walk with a certain mean and standard deviation. I can than predict the mean and variance (inclusive of parametric uncertainty) of the logit random walk. From this I can convert back into the market share scale, tracing out the mean path and a 90% predictive envelope.

It is the predictive envelop that I have not really explored before, but it is critical for understanding just how much uncertainty there is in a forecast that is properly conditioned on historical observation. If another modeler were to put forward a forecast that is substantially outside of the envelope, I would know that they are inserting information into there analysis that is not consistent with what has actually been observed since 2012. This does not mean that such a forecast is "wrong", but merely that it represents a substantial departure from historical trends. This departure may well originate in the modeler's own imagination about what make speed up or slow down the gain of market share in the future. For example, vehicle autonomy could alter the environment for EVs and ICE in ways that were not present in 2012 to 2018. Then again, Tesla has led with AP which may in fact give their cars a competitive advantage already witnessed in recent past. So these matters are largely a judgment call. I view my job as a modeler as to reveal what the data is telling us and to try not to assert my own opinions as an overlay to the data. This is the virtue of a truly statistical model over a judgmental model.

You can see the fruit of this simple analysis in the charts above. I have presented this with the y-axis as either on the nominal (market share) scale or the log market share scale. In the former, we see a typical logistic curve surrounded by the predictive envelope. Note that this envelope is widest around the year 2028. Yes, this is actually where we have the greatest uncertainty! Notice also that the envelope passes through the 50% mark between the years 2026 and 2030. Anything that might substantially accelerate or delay EV market dominance had better happen by 2025, otherwise it is just too late to make much of a difference. So if we imaging that autonomy will speed it up or "lack of public charging" (Come on, BNEF, you're better than that!) will slow it down, those things need to come into play within the next 5 years or it just won't impact he timing of market dominance

But some will look at that chart and think, "How can those tiny little historical observations blow up into such a big effect? I just can't believe that." This of course is the problem people have with intuiting exponential (or logistic) growth. So the whole forecast will strike them as a fanciful extrapolation. This is why I also present the exact same data with a log scale for market share. What is striking in the log scale is that one can see how the historical growth is strongly linear (or rather, log-linear). The pattern has been remarkably linear, and this is precisely why a random wake with drift is a compelling model. One is invited to question what exactly could take log market share off this strongly linear path. Indeed, one must see this clearly to understand why only very strong forces would really be able to knock it off course. For example, the oil crash of 2014-16 hardly makes a dint in the historical trend. To be sure it is there, but it is such a minor effect that one must look very closely for it. Of course, we know in the long run market share cannot exceed 100%, so ultimately the line must level out asymptotically. But notice that the bend does not really make much of difference until after EVs have dominated the market. Up through about 2025 EV growth will not be distinguishable from exponential growth.

In reality all forecasting is extrapolation. But if I must extrapolate, I prefer to extrapolate from data more so than from opinion. The trend is clear while the window to substantially alter the trajectory is narrow. When the data is bending to a different trajectory, I will gladly change my opinion. But for now the data are not showing signs of slowing, if anything the path is mildly speeding up.

Now let me make some predictions. 2019 PEV share comes in between 2.7% and 3.7%. Believable? How about 2020 between 3.8% and 6.1%, or 2022 between 7.4% and 14.8%. These may seem fairly wide, but not so wide as to be without consequence. Consider that BNEF is predicting on 10M EVs sold in 2025 or share of 10%. My model puts 2025 between 19% and 43%. So BNEF is already 2 standard deviations below my lower bound, which is about 2 standard deviations my mean of 29%. So BNEF is seriously bending the curve down in ways to which historical data does not bear witness.

Or let's back test this. In April, 2018, BNEF forecast 2018 to come in at 1.56M or about 1.67% share. The actual was 2.018M or 2.12% share. Let's what my method would have predicted using just 2012 thru 2015 data (a sample size of just 3). My 2015 forecast of 2018 would have centered on 2.2% with a 90% predictive interval from 1.3% to 3.5%. Yeah, a lot of uncertainty, but nailed it. My 2016 forecast of 2018 centered on 1.8% ranging from 1.3% to 2.5%. This was a little more pessimistic, but less uncertain. Then the 2017 forecast of 2018 centered around 2.0% ranging from 1.7% to 2.4%. So the predictive envelope nicely closed in on the actual. Meanwhile, BNEF low forecast was ruled out of the predictive interval by the time it was made using 2017 as the last historical datum. Presumably, BNEF's forecast had that benefit of highly granular data, their proprietary data, and a multi-industry team of analysts. Surely with all that going for them, they should have been able to produce a forecast with much less uncertainty, but in fact a sample size of 5 historical observations could have alerted them to the possibility that their prediction was high improbably.

So I am not at all saying that my very simple model is the best. That's not the point. The point is we need simple challenger models that don't assume much but capture the uncertainty of the historical record to tell us when our fancy forecasts are bending to improbable conclusions, overburdened with too much complexity, to much granularity, and to much opinion. If any modeling group out there wants to avoid embarrassing themselves with precious predictions, they would do well to stay with a predictive envelope such as I have constructed. This is what we call a challenger model. And to the rest of us, if we want to avoid being lured in by "credible" forecasts from well-funded organizations, we do well to have a few simple challenger models of our own.
 
@View attachment 410901 View attachment 410902

In an effort to make a simple, but compelling forecast of plug-in EV market share, I have boiled things down to a random walk on a logit scale. May sound complex, but it actually makes very few assumptions. I examine market share from 2012 to 2018, computing the annual increase in logit market share (log(S/(1-S)). So I've got now 6 data points. I compute the mean and sample standard deviations. I make the assumption that future logit differences have the same distribution as was operative in the past. Thus, the future is modeled as a random walk with a certain mean and standard deviation. I can than predict the mean and variance (inclusive of parametric uncertainty) of the logit random walk. From this I can convert back into the market share scale, tracing out the mean path and a 90% predictive envelope.

It is the predictive envelop that I have not really explored before, but it is critical for understanding just how much uncertainty there is in a forecast that is properly conditioned on historical observation. If another modeler were to put forward a forecast that is substantially outside of the envelope, I would know that they are inserting information into there analysis that is not consistent with what has actually been observed since 2012. This does not mean that such a forecast is "wrong", but merely that it represents a substantial departure from historical trends. This departure may well originate in the modeler's own imagination about what make speed up or slow down the gain of market share in the future. For example, vehicle autonomy could alter the environment for EVs and ICE in ways that were not present in 2012 to 2018. Then again, Tesla has led with AP which may in fact give their cars a competitive advantage already witnessed in recent past. So these matters are largely a judgment call. I view my job as a modeler as to reveal what the data is telling us and to try not to assert my own opinions as an overlay to the data. This is the virtue of a truly statistical model over a judgmental model.

You can see the fruit of this simple analysis in the charts above. I have presented this with the y-axis as either on the nominal (market share) scale or the log market share scale. In the former, we see a typical logistic curve surrounded by the predictive envelope. Note that this envelope is widest around the year 2028. Yes, this is actually where we have the greatest uncertainty! Notice also that the envelope passes through the 50% mark between the years 2026 and 2030. Anything that might substantially accelerate or delay EV market dominance had better happen by 2025, otherwise it is just too late to make much of a difference. So if we imaging that autonomy will speed it up or "lack of public charging" (Come on, BNEF, you're better than that!) will slow it down, those things need to come into play within the next 5 years or it just won't impact he timing of market dominance

But some will look at that chart and think, "How can those tiny little historical observations blow up into such a big effect? I just can't believe that." This of course is the problem people have with intuiting exponential (or logistic) growth. So the whole forecast will strike them as a fanciful extrapolation. This is why I also present the exact same data with a log scale for market share. What is striking in the log scale is that one can see how the historical growth is strongly linear (or rather, log-linear). The pattern has been remarkably linear, and this is precisely why a random wake with drift is a compelling model. One is invited to question what exactly could take log market share off this strongly linear path. Indeed, one must see this clearly to understand why only very strong forces would really be able to knock it off course. For example, the oil crash of 2014-16 hardly makes a dint in the historical trend. To be sure it is there, but it is such a minor effect that one must look very closely for it. Of course, we know in the long run market share cannot exceed 100%, so ultimately the line must level out asymptotically. But notice that the bend does not really make much of difference until after EVs have dominated the market. Up through about 2025 EV growth will not be distinguishable from exponential growth.

In reality all forecasting is extrapolation. But if I must extrapolate, I prefer to extrapolate from data more so than from opinion. The trend is clear while the window to substantially alter the trajectory is narrow. When the data is bending to a different trajectory, I will gladly change my opinion. But for now the data are not showing signs of slowing, if anything the path is mildly speeding up.

Now let me make some predictions. 2019 PEV share comes in between 2.7% and 3.7%. Believable? How about 2020 between 3.8% and 6.1%, or 2022 between 7.4% and 14.8%. These may seem fairly wide, but not so wide as to be without consequence. Consider that BNEF is predicting on 10M EVs sold in 2025 or share of 10%. My model puts 2025 between 19% and 43%. So BNEF is already 2 standard deviations below my lower bound, which is about 2 standard deviations my mean of 29%. So BNEF is seriously bending the curve down in ways to which historical data does not bear witness.

Or let's back test this. In April, 2018, BNEF forecast 2018 to come in at 1.56M or about 1.67% share. The actual was 2.018M or 2.12% share. Let's what my method would have predicted using just 2012 thru 2015 data (a sample size of just 3). My 2015 forecast of 2018 would have centered on 2.2% with a 90% predictive interval from 1.3% to 3.5%. Yeah, a lot of uncertainty, but nailed it. My 2016 forecast of 2018 centered on 1.8% ranging from 1.3% to 2.5%. This was a little more pessimistic, but less uncertain. Then the 2017 forecast of 2018 centered around 2.0% ranging from 1.7% to 2.4%. So the predictive envelope nicely closed in on the actual. Meanwhile, BNEF low forecast was ruled out of the predictive interval by the time it was made using 2017 as the last historical datum. Presumably, BNEF's forecast had that benefit of highly granular data, their proprietary data, and a multi-industry team of analysts. Surely with all that going for them, they should have been able to produce a forecast with much less uncertainty, but in fact a sample size of 5 historical observations could have alerted them to the possibility that their prediction was high improbably.

So I am not at all saying that my very simple model is the best. That's not the point. The point is we need simple challenger models that don't assume much but capture the uncertainty of the historical record to tell us when our fancy forecasts are bending to improbable conclusions, overburdened with too much complexity, to much granularity, and to much opinion. If any modeling group out there wants to avoid embarrassing themselves with precious predictions, they would do well to stay with a predictive envelope such as I have constructed. This is what we call a challenger model. And to the rest of us, if we want to avoid being lured in by "credible" forecasts from well-funded organizations, we do well to have a few simple challenger models of our own.

This is amazing. Makes me tiny bit more sanguine about outstanding bets I have with a couple of friends: if new sedans sales in the US are 50% EV or more by the end of 2022 I stand to collect a goodly amount of craft beer.

I look forward to seeing how the data track to your model. My training is in ecology and evolutionary biology, and I enjoy thinking about fundamental similarities between the forces that govern markets (especially re: energy transition and EVs) and those that govern ecological/evolutionary systems. I've been struck by how many of my colleagues - folks with an intimate understanding of logistic population growth and how selection acts on highly advantageous alleles - respond with rank disbelief and pessimism in response to even guarded optimism in discussions about the pace of energy transition and EV's in particular. Makes me wonder how much faster the transition could be if people were simply more optimistic about the future and less defeatist about our ability rise to the challenge. I suppose that's why the "D" in "FUD" is such an effective tool for delay.
 
Last edited:
@View attachment 410901 View attachment 410902

In an effort to make a simple, but compelling forecast of plug-in EV market share, I have boiled things down to a random walk on a logit scale. May sound complex, but it actually makes very few assumptions. I examine market share from 2012 to 2018, computing the annual increase in logit market share (log(S/(1-S)). So I've got now 6 data points. I compute the mean and sample standard deviations. I make the assumption that future logit differences have the same distribution as was operative in the past. Thus, the future is modeled as a random walk with a certain mean and standard deviation. I can than predict the mean and variance (inclusive of parametric uncertainty) of the logit random walk. From this I can convert back into the market share scale, tracing out the mean path and a 90% predictive envelope.

It is the predictive envelop that I have not really explored before, but it is critical for understanding just how much uncertainty there is in a forecast that is properly conditioned on historical observation. If another modeler were to put forward a forecast that is substantially outside of the envelope, I would know that they are inserting information into there analysis that is not consistent with what has actually been observed since 2012. This does not mean that such a forecast is "wrong", but merely that it represents a substantial departure from historical trends. This departure may well originate in the modeler's own imagination about what make speed up or slow down the gain of market share in the future. For example, vehicle autonomy could alter the environment for EVs and ICE in ways that were not present in 2012 to 2018. Then again, Tesla has led with AP which may in fact give their cars a competitive advantage already witnessed in recent past. So these matters are largely a judgment call. I view my job as a modeler as to reveal what the data is telling us and to try not to assert my own opinions as an overlay to the data. This is the virtue of a truly statistical model over a judgmental model.

You can see the fruit of this simple analysis in the charts above. I have presented this with the y-axis as either on the nominal (market share) scale or the log market share scale. In the former, we see a typical logistic curve surrounded by the predictive envelope. Note that this envelope is widest around the year 2028. Yes, this is actually where we have the greatest uncertainty! Notice also that the envelope passes through the 50% mark between the years 2026 and 2030. Anything that might substantially accelerate or delay EV market dominance had better happen by 2025, otherwise it is just too late to make much of a difference. So if we imaging that autonomy will speed it up or "lack of public charging" (Come on, BNEF, you're better than that!) will slow it down, those things need to come into play within the next 5 years or it just won't impact he timing of market dominance

But some will look at that chart and think, "How can those tiny little historical observations blow up into such a big effect? I just can't believe that." This of course is the problem people have with intuiting exponential (or logistic) growth. So the whole forecast will strike them as a fanciful extrapolation. This is why I also present the exact same data with a log scale for market share. What is striking in the log scale is that one can see how the historical growth is strongly linear (or rather, log-linear). The pattern has been remarkably linear, and this is precisely why a random wake with drift is a compelling model. One is invited to question what exactly could take log market share off this strongly linear path. Indeed, one must see this clearly to understand why only very strong forces would really be able to knock it off course. For example, the oil crash of 2014-16 hardly makes a dint in the historical trend. To be sure it is there, but it is such a minor effect that one must look very closely for it. Of course, we know in the long run market share cannot exceed 100%, so ultimately the line must level out asymptotically. But notice that the bend does not really make much of difference until after EVs have dominated the market. Up through about 2025 EV growth will not be distinguishable from exponential growth.

In reality all forecasting is extrapolation. But if I must extrapolate, I prefer to extrapolate from data more so than from opinion. The trend is clear while the window to substantially alter the trajectory is narrow. When the data is bending to a different trajectory, I will gladly change my opinion. But for now the data are not showing signs of slowing, if anything the path is mildly speeding up.

Now let me make some predictions. 2019 PEV share comes in between 2.7% and 3.7%. Believable? How about 2020 between 3.8% and 6.1%, or 2022 between 7.4% and 14.8%. These may seem fairly wide, but not so wide as to be without consequence. Consider that BNEF is predicting on 10M EVs sold in 2025 or share of 10%. My model puts 2025 between 19% and 43%. So BNEF is already 2 standard deviations below my lower bound, which is about 2 standard deviations my mean of 29%. So BNEF is seriously bending the curve down in ways to which historical data does not bear witness.

Or let's back test this. In April, 2018, BNEF forecast 2018 to come in at 1.56M or about 1.67% share. The actual was 2.018M or 2.12% share. Let's what my method would have predicted using just 2012 thru 2015 data (a sample size of just 3). My 2015 forecast of 2018 would have centered on 2.2% with a 90% predictive interval from 1.3% to 3.5%. Yeah, a lot of uncertainty, but nailed it. My 2016 forecast of 2018 centered on 1.8% ranging from 1.3% to 2.5%. This was a little more pessimistic, but less uncertain. Then the 2017 forecast of 2018 centered around 2.0% ranging from 1.7% to 2.4%. So the predictive envelope nicely closed in on the actual. Meanwhile, BNEF low forecast was ruled out of the predictive interval by the time it was made using 2017 as the last historical datum. Presumably, BNEF's forecast had that benefit of highly granular data, their proprietary data, and a multi-industry team of analysts. Surely with all that going for them, they should have been able to produce a forecast with much less uncertainty, but in fact a sample size of 5 historical observations could have alerted them to the possibility that their prediction was high improbably.

So I am not at all saying that my very simple model is the best. That's not the point. The point is we need simple challenger models that don't assume much but capture the uncertainty of the historical record to tell us when our fancy forecasts are bending to improbable conclusions, overburdened with too much complexity, to much granularity, and to much opinion. If any modeling group out there wants to avoid embarrassing themselves with precious predictions, they would do well to stay with a predictive envelope such as I have constructed. This is what we call a challenger model. And to the rest of us, if we want to avoid being lured in by "credible" forecasts from well-funded organizations, we do well to have a few simple challenger models of our own.

Amazing stuff! Thanks for sharing your knowledge and insight. :)

giphy.gif
 
This is amazing. Makes me tiny bit more sanguine about outstanding bets I have with a couple of friends: if new sedans sales in the US are 50% EV or more by the end of 2022 I stand to collect a goodly amount of craft beer.

I look forward to seeing how the data track to your model. My training is in ecology and evolutionary biology, and I enjoy thinking about fundamental similarities between the forces that govern markets (especially re: energy transition and EVs) and those that govern ecological/evolutionary systems. I've been struck by how many of my colleagues - folks with an intimate understanding of logistic population growth and how selection acts on highly advantageous alleles - respond with rank disbelief and pessimism in response to even guarded optimism in discussions about the pace of energy transition and EV's in particular. Makes me wonder how much faster the transition could be if people were simply more optimistic about the future and less defeatist about our ability rise to the challenge. I suppose that's why the "D" in "FUD" is such an effective tool for delay.
I'd be careful about betting on individual countries and segments. These can have much noisier paths than the global market. The more volatility, the wider the prediction envelope. That said, US sedans are looking more established and set for a reliable run up. If you like, you can send me market share data pertinent to your bet, and I let you know what my analysis can do with it.

Yeah, I think ecology is highly relevant to disruptive technology. This even explains why I prefer to do an analysis at the global level, rather than trying to sum up a bunch of local markets. Basically any market that is pushing the technology, competition and ramping up capacity is creating highly successful species. Highly successful competitors will snap up demand in a few markets and then migrate out into other markets. When they migrate to a new market, things can change quite abruptly locally. Think about Model 3 just hitting North America, big jump. Then it migrates to China and EU, more big jumps. If you were just trying to forecast one country, you'd see these big jumps that seem to come out of nowhere. But if you forecast globally, you done have to worry so much about big surges in supply or market saturation. Indeed local market saturation resolves itself by producers shipping product to less saturated markets.

We see Tesla doing this, but the big event I think will be when the Chinese EV market becomes so developed and maybe a touch crowded, that China start massively exporting EVs. This could lead to some pretty rapid transition in national markets. EV makers first have to compete with each other and with Tesla. Huge selective pressure will advance the industry. But the domestic market is still so vast and well supported by policy. When they get hungry for new markets, watch out.

So it's not necessary for every local market to be making steady, predictable progress. The key thing is that the global battery supply is ramping and new products are hitting the market. Then that battery supply flows into whatever models and markets that are willing to pay the most for them. This is fundamentally a global supply market, just like big screen TVs or solar panels.

Good luck with your bet.
 
I'd be careful about betting on individual countries and segments. These can have much noisier paths than the global market. The more volatility, the wider the prediction envelope. That said, US sedans are looking more established and set for a reliable run up. If you like, you can send me market share data pertinent to your bet, and I let you know what my analysis can do with it.

Yeah, I think ecology is highly relevant to disruptive technology. This even explains why I prefer to do an analysis at the global level, rather than trying to sum up a bunch of local markets. Basically any market that is pushing the technology, competition and ramping up capacity is creating highly successful species. Highly successful competitors will snap up demand in a few markets and then migrate out into other markets. When they migrate to a new market, things can change quite abruptly locally. Think about Model 3 just hitting North America, big jump. Then it migrates to China and EU, more big jumps. If you were just trying to forecast one country, you'd see these big jumps that seem to come out of nowhere. But if you forecast globally, you done have to worry so much about big surges in supply or market saturation. Indeed local market saturation resolves itself by producers shipping product to less saturated markets.

We see Tesla doing this, but the big event I think will be when the Chinese EV market becomes so developed and maybe a touch crowded, that China start massively exporting EVs. This could lead to some pretty rapid transition in national markets. EV makers first have to compete with each other and with Tesla. Huge selective pressure will advance the industry. But the domestic market is still so vast and well supported by policy. When they get hungry for new markets, watch out.

So it's not necessary for every local market to be making steady, predictable progress. The key thing is that the global battery supply is ramping and new products are hitting the market. Then that battery supply flows into whatever models and markets that are willing to pay the most for them. This is fundamentally a global supply market, just like big screen TVs or solar panels.

Good luck with your bet.

Ha - thanks. No matter who wins we'll probably share the beer, so it's pretty low stakes :) I appreciate the offer re: segment data.

Metapopulation/community dynamics (e.g. attached review paper) seem like such a great analog for this sort of thing, and I really like your explanation with Model 3. Works well for thinking about solar/battery storage too. A patchwork of communities (countries, states), each with a distinct selective environment determined by some combination of biotic and abiotic factors (i.e. government policy, competing technologies, consumer preference, solar resource, etc). Germany and California, for example, acting as source populations wherein selection favors the deployment of new solar technologies at a higher cost than other patches would support. As technologies descend the cost curve in these populations - perhaps concurrent with changing conditions in other patches (e.g. rising oil prices) - dispersal from source patches gains a foothold elsewhere, initially among a small ecological niche (early adopters), and then rapidly filling others.

Another fun one to chew on: Read Gould's The Misnamed, Mistreated, and Misunderstood Irish Elk and replace the Irish Elk in your mind with legacy automakers (especially the Big Three with their focus on big showy trucks). It examines how the conflict between sexual selection (analog: chasing short-term profits and existing market) and natural selection (analog: optimizing for efficiency both in business operations and vehicles) left Megaloceros giganteus unable to adapt to rapid climate change, driving it to extinction (seems apropos).

Anyhow, I fear I'm veering off-thread, so I won't clog it up with this stuff any more than I already have, but lacking a background in finance or business it's been a useful lens for me as I try to understand the big picture being discussed here.
 

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Part of what makes this thread work so well, is it's got contributors with lots of different backgrounds, making connections between lots of different ways of thinking about problems. Obviously lots of finance and business, and closely related (micro / macro economic for instance), but also biology / ecology and more I can't think of now.

Off-thread is hard (my opinion) if you're adding a new way of thinking about something, especially when it includes links / source material, and you're creating possibilities for people to get smarter / think about things in new ways than were previously available.
 
China Nev Logistic.png


China NEV Log Logistic.png


EV-Volumes - The Electric Vehicle World Sales Database
China EV Forecast: 50% EV Market Share by 2025 — Part 3, Ramping Production | CleanTechnica

Let's turn to China. EV-Volumes.com and Dr Maximilian Holland have done the hard spadework of amassing data and arguing a "business" case for how and why NEVs will keep growing in China. I get to do the easy work of computing a couple of nifty stats and making pretty charts.

I get my historical data from EV-Volumes.com. They estimate 2018 as 4.4% NEV penetration and forecast that this will grow to 6.7% in 2019.

Dr Holland makes a more ambitious case for 7.5% in 2019, more than 10% in 2020 and more than 50% in 2025. Indeed the predictions of both groups are entirely in line with ultra-simplistic extrapolation from sample size 4 data points. This is clearly seen in the charts above.

The third chart is a backtest using 2017 as the last historical datum, a whopping N=3. You may ask me how I can do that. Well, I'm a highly trained statistics professional; don't try this at home. Here the point of the backtest is to see if the method can deliver reasonable results even with less data. Incredibly the central path is hardly moved. The 90% confidence prediction envelope is quite sufficient. Part of the secret sauce here is using a T distribution with 2 degrees of freedom, rather than a normal distribution for setting the confidence limits. Yes, you learned that in your elementary statistics class, and this is just the kind of situation where we care a able small sample niceties.

Backtest
China NEV backtest.png


Moving back to the current forecasts (2018 last historical date), we see that the trend from 2014-2018 clearly supports hitting 50% market share in 2024/25. For 2019, this forecast is centered on 6.9%, ranging 4.2% to 11.3%. I think it is fair to say that a more detailed boots-on-the-ground analysis ought to be able to narrow the predictive range substantially. For 2020, this forecast centers on
10.8% and ranges from
5.1% to 21.6%. Yep, very wide, but that's all you get from N=4. 2022 centers on 24.2% and ranges from 7.7% to 55.0%. Now this should be frightening to the oil industry. The upper side of this range clearly puts demand growth at risk, at least in China. Imagine the gnashing of teeth that would ensue if oil demand were to peak in China in 2022. Finally for 2025, my forecast centers on 57.7% and ranges from 14.5% to 91.6%.

Ok, this last interval is frustratingly wide. This is where a couple more observations would really help narrow the envelope. Like the second season of Lost, we can now flash forward to provide a "pretest" (or would that be a "foretest"?). What would our next forecast be if 2019 does come in at 6.7% as EV-Volumes suggests? We can do this. The 2019 pretest would have a forecast for 2022 that centers on 23.0% and ranging from 11.6% to 40.5%. This narrow the range by about 18% points, just from increasing the sample size from 4 to 5. Also the pretest forecast for 2025 centers on 55.5% and ranges from 24.3% to 83.0%. This range is 59% down from 77% from the current forecast. What's critical here is that the lower end of the predictive range has gone from 14.5% to 24.3%. So we'd have much more confidence in the idea that China will have peaked by 2025. Now, if Dr Holland's forecast of 7.5% for 2019 holds, our forecast also gets a bump up. The lower limit rises to 29.0% with the center at 62.0% and upper limit at 86.7%. The sensitivity is interesting here: 2019 coming in at 7.5% rather than 6.7% may not seem like much of a difference, but it boosts the lower prediction for 2025 to 29.0% up from 24.3%. A short term 0.8% boost makes a 4.7% difference in the downside to 2025. I think this illustrates just how important fighting for every EV sale today is.

Pretest?
China NEV pretest.png


Ok, so if you are still reading, you have indulged my geeky side. Thanks. The bottom line for this post is that China is growing aggressively. It is well on track to EV dominance (more than 50% NEV market share) by 2025. The predictive envelope is still quite wide, and this is primarily of function of have too few years of observation. This is statistical uncertainty more so than it is real volatility. So in the next year, these forecasts will narrow considerably. We continue to benefit from research that detail how EV makers will be able to scale in practical terms. It is a huge task, and we need to know that there are realistic pathways to this end. But alas the trend is clear. China might well arrive at EV dominance before the US does, but that will require looking at US results.
 
Ha - thanks. No matter who wins we'll probably share the beer, so it's pretty low stakes :) I appreciate the offer re: segment data.

Metapopulation/community dynamics (e.g. attached review paper) seem like such a great analog for this sort of thing, and I really like your explanation with Model 3. Works well for thinking about solar/battery storage too. A patchwork of communities (countries, states), each with a distinct selective environment determined by some combination of biotic and abiotic factors (i.e. government policy, competing technologies, consumer preference, solar resource, etc). Germany and California, for example, acting as source populations wherein selection favors the deployment of new solar technologies at a higher cost than other patches would support. As technologies descend the cost curve in these populations - perhaps concurrent with changing conditions in other patches (e.g. rising oil prices) - dispersal from source patches gains a foothold elsewhere, initially among a small ecological niche (early adopters), and then rapidly filling others.

Another fun one to chew on: Read Gould's The Misnamed, Mistreated, and Misunderstood Irish Elk and replace the Irish Elk in your mind with legacy automakers (especially the Big Three with their focus on big showy trucks). It examines how the conflict between sexual selection (analog: chasing short-term profits and existing market) and natural selection (analog: optimizing for efficiency both in business operations and vehicles) left Megaloceros giganteus unable to adapt to rapid climate change, driving it to extinction (seems apropos).

Anyhow, I fear I'm veering off-thread, so I won't clog it up with this stuff any more than I already have, but lacking a background in finance or business it's been a useful lens for me as I try to understand the big picture being discussed here.
Nice read. Thanks. Surely the goodness of God would not allow oil men to suffer the same fate as well endowed elk.
 
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Asia oil refiners mull run cuts with margins at 16-year low for season: sources, data - Reuters
Red Flag For Oil Markets: Asian Refining Margins Plunge To 16-Year Low | OilPrice.com

Independent "tea pot" refiners in China are faced with the following:
* Export quotas, limited to 50Mt of exportable refined oil products
* Domestic glut of refined oil products
* Rising crude prices

All this is killing refiner margins and production has fallen to 50% of capacity. The export quotes can be increased by the government. This will be an increasingly needful remedy. Tea pots import about 20% of total crude imports to China. So at least in the short run, without export quotas, refiners have the potential to export an incremental half of their capacity. Thus more than 10% of crude China imports could be added to refined product export. China's oil import in 2018 was 9.3 mb/d. So if my math is right, teapots have the spare capacity to bring incrementally roughly 1 mb/d of refined products to the export market.

So if the Chinese government where to grow frustrated with a higher price of crude, it basically has within its power to flood the refined export market with an extra 1 mb/d. This should be seen as a threat to refiners everywhere. Relaxing the export quota would bring down refiner margins around the world, but especially in Asia. This in turn would reduce production everywhere and kill off global demand for crude outside of China.

Actually, the total volume demand for export crude would change very little, but the margin pain that teapots are experiencing would spread out to all refiners. The likes of Saudi Aramco would not like this as they have been investing heavily in building out refinery capacity. The unavoidable problem the industry faces as oil demand plateaus is that there is a glut of refiner capacity. These are long lived capital heavy assets. The teapot capacity glut in China is largely a consequence of government dictate over that industry. So they are feeling the pain while other players are expanding capacity elsewhere on the planet. As China's oil demand plateaus and goes into decline, This teapot capacity becomes a risk to all refiners. The government has a powerful bargaining chip to play against any country that is heavily invested in oil refining or crude production. And let's not forget that the government has a oil reserve. So they have a lever on demand for crude as well as a lever on the supply of refined products.

But as domestic demand declines in China, and China is faced with huge opportunities to export EV batteries, EVs, and other clean tech, how will the government use the leverage is has on oil markets?
 
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U.S. Shale Oil Production Set To Grow 16% This Year | OilPrice.com

Rystad is predicting US shale oil adds 1.1 to 1.2 mb/d this year.

Yup, that pretty much covers global demand growth right there. While we've got plenty of reasons to question how long shale can keep this up, it's still quite an achievement that they keep doing this. WTI under $50/b seemed to slow their pace a bit, but they seem quite willing and able to oversupply the market for $60/b.

US shale is being run on a "money is no object" basis, like the US war effort in WWII or the Manhattan Project. Makes you wonder if it's actually military-funded, though I see no evidence of that -- it seems that it's just dumb investors!
 
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