Forget next year’s commodity prices: focus on 2075’s

Overview

In order to help manage risks, form fiscal and monetary policies and anticipate (and thereby profit from) changes of economic activity, some producers, many governments and central banks – and most speculators – attempt to predict short-term (that is, over periods of up to one year) fluctuations of commodity prices. The more reliable are these forecasts, the better will be producers’ decisions, governments’ fiscal and central banks’ monetary policies, and the more profitable will be speculators’ bets, etc.

Yet there’s a severe and insurmountable problem: like stocks’ 12-month returns, the short-term fluctuations of commodities’ prices are mostly random and thus usually unpredictable. Hence forecasters are wasting their time – and those who take their forecasts seriously are risking poor decisions.

“The one certainty about the forecast deficits in Tuesday’s budget,” wrote Stephen Bartholomeusz (“Treasury’s commodity price forecasts are destined to be wrong,” The Sydney Morning Herald, 30 March 30, 2022), “is that the outcomes will be different from the forecasts, perhaps wildly. That’s because some of the key swing factors in the outcomes are so unpredictable that Treasury essentially doesn’t try to predict them, instead inputting ultra-conservative forecasts that invariably are proven wrong and more often than not badly wrong.”

Bartholomeusz concludes: “the range of variables to consider and the scenarios that could develop, even in the short term, make the task of predicting commodity prices – or budget outcomes with any precision – near-impossible.”

These prices’ short-term variability is so wide that “experts” simply can’t know – and if they were brutally honest, would candidly admit that they don’t know – what the price of coking and thermal coal, copper, crude oil, iron ore, LNG, etc., will be over the next year. Whether it’s knowingly or unwittingly, they’re simply guessing. “There are two kinds of forecasters,” John Kenneth Galbraith acidly but astutely reckoned: “those who don’t know, and those who don’t know they don’t know.”

Leithner & Company knows that forecasters don’t know. We therefore ignore short-term commodity price forecasts – and our valuations of leading energy and mining companies exclude them.

We also discern what forecasters apparently overlook: it’s possible to make plausible inferences about prices’ CPI-adjusted compound annual growth rates (CAGRs) over the next 10, 20 and even 50 or more years. These inferences contribute to our valuations of major energy and mining companies; this article derives and justifies them.

Hence commodity price forecasters – and those who take them seriously – make two glaring errors: firstly, they obsessively attempt to foretell what’s unpredictable; secondly, they resolutely ignore what’s roughly foreseeable.

CPI Adjustment

Throughout this article I adjust commodities’ prices, and both short- and long-term fluctuations of their prices, for the Consumer Price Index. For brevity, unless otherwise indicated all of my results are in CPI-adjusted $US. Two points are most relevant:

1. CPI measures the change over time of the prices paid by consumers for equivalent baskets of consumer goods and services. Analysis of commodity prices in “real” terms removes the impact of consumer price inflation from commodity price changes, and thus isolates supply and demand factors that affect prices.
2. It’s important to distinguish short-term volatility from long-term trends. Various factors, such as weather-related supply disruptions, geo-political instability, etc., cause commodities’ prices to fluctuate in short and medium terms (five years); crucially, however, and as I’ll show, after adjusting for consumer price inflation these fluctuations seldom produce large long-term price changes.

In the Short Term, Nobody Can Predict Commodity Prices

In Stop kidding yourself: Nobody can “time the market” (30 June), I wrote: “except under rare circumstances, market-timers can’t consistently speculate successfully. That is, they can’t repeatedly and accurately anticipate short-term movements of a market index’s level or a stock’s price. That’s because these fluctuations reflect mere chance. Random movements are irregular variations around a mean. They have no identifiable cause and are trendless; hence they’re unpredictable. Successful speculations are merely lucky guesses; these ‘successes’ are therefore ephemeral.” 

Like market indexes’ and individual stocks’ short-term returns, so too with commodity prices’ short-term fluctuations: market-timing fails because it ignores an obvious, fundamental and insurmountable difficulty. Price fluctuations are random; by definition, random phenomena are unforeseeable – and nobody can predict what’s inherently unpredictable.

Using data compiled by the World Bank, I’ve analysed the monthly prices (denominated in $US) of Australia’s major energy and mining commodity exports – coal, copper, LNG and iron ore – since 1960. I’ve computed their CPI-adjusted, rolling 12-month percentage price changes (that is, for January 1960-January 1961, February 1960-February 1961 ... and June 2024-June 2025). For more than a decade, iron ore has been Australia’s single largest export. Figure 1 plots the short-term fluctuations of its price.

Figure 1: CPI-Adjusted, Rolling 12-Month Percentage Change, Spot Price of Iron Ore, January 1961-June 2025

Over the years, iron ore’s price hasn’t merely fluctuated; it’s oscillated much more since January 2004 than it did previously.

For the entire 64-period, its rolling 12-month percentage change has averaged 2.9%; from 1961 to 2003, it averaged -1.5%, and since 2004 it’s averaged 11.6%. Similarly, for the entire period the percentage changes’ standard deviation is 28.0%; from 1961 to 2003, however, it was 8.8%, and since 2004 it’s been 45.7%.

The more these price fluctuations’ distribution resembles a normal (that is, bell-shaped) distribution, the more random they are – and the more unpredictable future fluctuations will be.

A Quantile-Quantile (“QQ”) plot provides one means to assess the extent to which a variable’s observations are normally distributed. (A quantile is a value below which a given percentage of data falls.) It compares the quantiles of a commodity’s price movements to those of a normal distribution whose mean and standard deviation equal the prices changes’. If the fluctuations are normally distributed, the points on the QQ plot will fall along a straight line.

Iron ore’s QQ plot (Figure 2) indicates that its rolling 12-month percentage price changes over the past 64 years haven’t been – when compared to Australian and American equity indexes’ total returns – perfectly normally distributed. The indexes’ lines of best fit have R² values of 0.99 (the maximum possible score is 1.0). In contrast, iron ore’s is just 0.77. In particular, its distribution’s upper tail (which in a normal distribution conventionally comprises the top-2.5% of observations) isn’t merely “fat” – its upper quarter is grossly distended.

Figure 2: Quantile-Quantile Plot, Iron Ore’s CPI-Adjusted 12-Month Percentage Change, January 1961 to June 2025

Figure 3a: QQ Plot, Iron Ore’s CPI-Adjusted 12-Month Percentage Change, January 1961 to December 2003

What are Figure 3a and Figure 3b telling us? Ultimately, two things:

1. The distributions have “fat tails.” Usually – that is, except when the 12-month percentage gains or losses are large – their observations are random and thus unpredictable.
2. Only infrequently does market timing become possible: specifically, extremely high or low returns in one 12-month period reliably (but not invariably) beget a regression towards the mean in subsequent periods.

Figure 3b: QQ Plot, Iron Ore’s CPI-Adjusted 12-Month Percentage Change, January 2004 to June 2025

In Stop kidding yourself: Nobody can “time the market” (30 June), I wrote: “I’ve not found a company (or major market index) whose short-term returns, measured over long intervals such as 20 or more years, aren’t nearly random (albeit with “fat tails”). Accordingly, to my knowledge there’s no company whose total 12-month returns are anything other than unpredictable the vast majority of the time.”

Similarly (for the sake of brevity I’ve omitted the details), there’s no major commodity which trades in liquid “spot” markets – including Australia’s major mineral and energy commodity exports but excluding lithium and uranium – whose short-term fluctuations are anything other than unpredictable the vast majority of the time.

What We Know, Don’t Know and Can Infer

Before we proceed, let’s distinguish what we (1) can reasonably assume that we know, (2) certainly don’t know and (3) don’t know but can reasonably infer. We know that commodities’ short-term fluctuations approximate (albeit with “fat tails”) standard normal distributions; we also know that the distribution of iron ore’s 12-month fluctuations since 2004 has a mean of 11.6% and a standard deviation is 45.7%. In contrast, we don’t know what the percentage change of iron ore’s price will be 12 months hence; that’s because we know that past returns have been – and, we assume, future ones will be – largely random.

Individual observations from a random distribution are unpredictable; but precisely because they follow a random distribution, large numbers of these observations will tend to follow a pattern.

The roll of a fair die provides a simple analogy: although you can't know the outcome of the next roll, if you roll many times it’s reasonable to infer that each number (1 through 6) will appear one-sixth of the time, “3” or below and “4” and above will occur one-half of the time, etc.

Assuming that it derives from a normal distribution whose mean is 11.6% and standard deviation is 45.7%, we don’t know what the percentage change of iron ore’s price over the next 12 months will be; we can, however, infer the probability that particular ranges of fluctuations will occur. For example, there’s a ca. one-in-three chance that the percentage change will be negative, and a ca. two-in-three chance that it’ll be positive.

Clearly we must infer cautiously. If the distribution were perfectly normal, and assuming that future fluctuations derive from a normal distribution with the same mean and standard distribution, there’s a 95% chance that fluctuation over the next 12 months will lie within ± two standard deviations of its mean, i.e., within the range 11.6% + (2 × 45.7%) = 103.0% and 11.6% - (2 × 45.7%) = -79.8% Hence there’s a 2.5% probability that it will be less than -79.8%, and a 2.5% chance that it’ll be greater than 103.0%.

But given the distribution’s “fat tails,” we’re underestimating the likelihood of extreme results. That’s one reason why hedge funds underperform – and many fail (see How Warren Buffett has trounced “the world’s greatest hedge fund manager” 11 August),

Commodity market-timers and speculators are trying, perhaps unwittingly, to predict what’s mostly unpredictable. Like those who claim they can “time” equity markets, they’ve been fooled by randomness. That’s why they’re occasionally lucky – but those who heed their advice virtually always lose.

Over Very Long Terms, Commodities’ CPI-adjusted Prices Change Little

In 1938, in response to concerns about limited reserves and the desire to retain them for domestic use, the Commonwealth government banned the export of iron ore; in 1960, in the wake of the discovery of large deposits in the Pilbara the trade treaty with Japan which it had signed in 1957, Canberra lifted the embargo. During the next 20 years, in response to strong demand from Japan, and to lesser extents Korea and Taiwan, exports rose rapidly.

It’s not widely known: Australia’s exports of iron ore to China commenced in the 1970s, but for more than a decade thereafter they grew slowly. Only in the late-1990s did China’s demand become voracious. Conversely, virtually everybody knows that by the turn of the century these had become a significant force in the Australian economy – and that since 2011 they’ve been its biggest source of exports.

Using data compiled by the World Bank, Figure 4 plots iron ore’s CPI-adjusted “spot” price since January 1960. Its minimum ($52) occurred from July to December of 2002, and its maximum ($299) in November 2007. Four points are most relevant:

1. In January 1960, its spot price was $126; in December 2002, it was $52; in June of this year, it was $92. Over the 42 years to December 2003, its compound annual growth rate (CAGR) was -2.0% per year; over the 65.5 years to June 2025, its CAGR was -0.6% per year.
2. Between January 1960 and December 2003, the price averaged $85; since January 2004, it’s averaged $142.
3. Between January 1960 and December 2003, the price’s variation (12-month standard deviation) averaged $22; since January 2004, it’s averaged $61.
4. Points #2 and #3 (and myriad other short- and medium-term influences upon iron’s ore’s spot price) don’t greatly affect – still less do they overturn – point #1.

Figure 4: Spot Price, Iron Ore, Monthly, CPI-Adjusted $US, January 1960-June 2025

Point #1 surprises many people. It shouldn’t: over very long periods, commodities’ CPI-adjusted CAGRs, including those of Australia’s major exports (Figure 5), typically rise little and often fall slightly.

Figure 5: CPI-Adjusted CAGRs, Spot Prices of Australia’s Major Export Commodities

Dramatic short- and medium-term exceptions – each commodity has its list; I summarise one in the next section – mustn’t divert our attention from the hum-drum rule: over the decades, as methods of production become more efficient and new sources of supply emerge, commodity prices stagnate or fall.

Before proceeding, note another crucial point: Figure 1 showed that, for the entire 64-period, iron ore’s rolling 12-month percentage change has averaged 2.9%. Figure 5 shows that its CAGR since 1960 has been -0.5% per year, and since 1975 has been -0.3% per year. This disparity is largely the consequence of the adjustment for CPI; partly, it also reflects the difference between arithmetic and geometric means.

An investment’s average result ALWAYS exceeds – often greatly – its most likely result. The same is true of commodity prices’ fluctuations. For a full explanation and implications, see How you – and managed funds – overstate your returns (17 October 2024).

Let’s now consider points #2 and #3 from Figure 4. Why has the CPI-adjusted price of iron ore been much higher since ca. 2004 than it was previously? Why has the price fluctuated much more than it did before (refer also to Figure 1)? The most important word in any answer to the first question is “China;” and the key phrase in any answer to the second question is “spot pricing.”

As we consider these two developments, it’s important to keep in mind the most fundamental point: Australia’s “once in a century” mining boom has – perhaps permanently – increased iron ore’s spot price; however, and notwithstanding China’s voracious demand, this commodity’s 50-year and 65-year CAGRs remain negative.

Given China’s dominant position as the world’s largest importer (over the past decade, it’s absorbed approximately 70% of the global seaborne iron ore trade), direct demand from its steel mills – and indirect demand from its construction firms, property developers, etc., who consume the steel – significantly influences iron ore’s price.

Fluctuations of Chinese demand, driven by factors such as infrastructure spending, manufacturing activity and conditions in real estate markets – as well as government “stimulus” of these factors – can also trigger considerable price volatility (for a current example, see China’s mega dam unleashes ‘flurry’ of bullish iron ore trades, The Australian Financial Review, 22 July).

Conversely, a slowdown of steel production, as occurs during periods of actual or apprehended economic weakness, downturns of the property market, etc., can cause spot iron ore prices to fall. The traditional seasonality of Chinese demand, which occurs as a result of lower construction activity during summer, etc., also influences prices.

“The iron ore spot market has become increasingly important for Australian exporters over recent years,” noted the RBA (“An Update on Australian Iron Ore Price-Setting Arrangements,” 1 June 2012). “Based on company reports and market analysts, we estimate that monthly contracts and spot market sales now account for close to 60% of all iron ore exports, compared with 20-30% in mid 2010, and almost nil prior to 2009 when annual contracts were central in price-setting.”

Spot prices are generally more volatile than contract prices such as those which the Australian producers and Chinese, Japanese, etc., consumers of iron ore set annually before 2009.

Real-time supply and demand determine spot prices; accordingly, they can and do fluctuate rapidly in response to market conditions. Contract prices, in contrast, consider various factors beyond immediate market conditions, including storage costs, interest rates and expectations about future supply and demand. These considerations typically tamp contract price movements compared to spot prices.

Today, Australia’s iron ore exporters release little information about their pricing mechanisms. Bearing this caveat in mind, BHP seems to sell a significant minority (approximately 10-20%) of its output in the spot market. Most (ca. 60%) of its sales are tied to monthly contracts whose prices reflects the previous month’s average spot price. For the remainder (20-30%) of its sales, it employs a mix of quarterly contracts and index-based pricing.

Clearly, spot pricing in the global iron ore market is much more prevalent today than it was 20 years ago; partly as a result (generally voracious but variable demand from China has also played a major role), the spot price fluctuates much more now than it did then.

Over Shorter Intervals, Commodity Prices Occasionally Boom and Bust

Given the inelasticity of short-term supply and demand, factors such as unexpectedly strong demand or sudden disruption of supply can cause commodity prices suddenly to skyrocket. During slowdowns, recessions and financial crises, on the other hand, prices often plunge.

The global crude oil market is the world's largest (by revenue) commodity market. Its trading volumes typically exceed $8.5 billion per business day; that’s ca. $2.2 trillion per year. Its dominance reflects oil’s irreplaceable role in transport and industrial production, and its significance in some parts of the world in electricity generation.

Affordable oil (and, more generally, hydrocarbons including coal, gas and LNG) is a basis of modern life and a prerequisite of economic growth.

Like any market, the global crude oil market’s key characteristics include the interplay of supply and demand. Imbalances, particularly those triggered by geopolitical events (or the fear or threat that such events will cause imbalances), can cause dramatic fluctuations of prices. Perhaps most notably, in October 1973 and in response to the Yom Kippur War (which erupted when Egypt and Syria attacked Israel), Arab member states of OPEC (Organisation of Petroleum Exporting Countries) slashed their production of crude oil and banned exports to countries including the U.S. and the Netherlands.

OPEC’s Arab member states, primarily Saudi Arabia, sought to exert pressure upon Western nations, especially the U.S., to change their policies regarding the conflict and support a withdrawal of Israeli forces from the territories they’d captured. (OPEC’s non-Arab members, Iran, Nigeria and Venezuela, declined to support the embargo.) In response, the price of Brent crude skyrocketed almost five-fold in nominal terms – from $2.70 per barrel in September 1973 to $13 in February 1974. In CPI-adjusted terms, the price zoomed from $19 to $90 (Figure 6).

Figure 6: Spot Price, Brent Crude Oil, Monthly, CPI-Adjusted $US, January 1960-June 2025

This upsurge triggered a global oil crisis, an eruption of consumer and producer price inflation and recessions in most Western nations including Australia.

An even bigger crisis occurred towards the end of the 1970s: the overthrow of the Western-oriented Shah of Iran by Islamist revolutionaries in 1978-1979 severely reduced that country’s oil exports. It thereby disrupted the world’s supply and caused the decade’s second oil crisis. In response, other OPEC member nations, particularly Saudi Arabia, boosted their exports; nonetheless, although the decrease of production on a global basis was relatively small (ca. 4%), the fear of shortages and uncertainty surrounding future supply triggered widespread panic.

In nominal terms, Brent crude’s price skyrocketed from $12.85 per barrel in October 1978 to $40.75 in November 1979; in CPI-adjusted terms, it catapulted from $62 to $178.

The Iran-Iraq War, which erupted in 1980 and raged for much of the decade, caused intermittent and near-total shutdowns of oil exports from both countries; it thereby exacerbated and considerably lengthened the crisis. Not until February 1986 did crude oil’s price – whether nominal or CPI-adjusted – return to the level which had prevailed in October 1978.

This sharp rise was a major contributor to the “double-dip” recessions in Western countries including Australia and the U.S. in the early-1980s.

Events in the Middle East ultimately caused the oil crises, but central banks’ “accommodation” was a necessary condition of these crises. As Milton Friedman emphasised at the time, without lax monetary policy the two oil shocks couldn’t have generated sharply higher and long-lasting consumer price inflation. Had the supply of money remained relatively fixed, a shift in relative prices (higher for energy and lower for most other goods, but no rise of the general price level) would have occurred.

In 2008 – also a time when central banks let the supply of money gallop – a combination of factors (increased global demand, primarily from China and India, as well as rising concerns about limited production capacity) caused crude oil’s price to surge. Brent crude zoomed from $92 in January 2007 to an all-time high of $206 in June and July 2008. Geo-political factors (such as the insurgency against foreign troops in Iraq) and the falling value of the $US and also contributed to the spike.

The GFC, however, caused the price to collapse: by March 2009, it fell as low as $66. Finally, the COVID-19 pandemic and lockdown caused Brent’s price to crash (to $29 in April 2020) and central banks’ panic and unprecedented irresponsibility, together with the Russian invasion of Ukraine, cause it to skyrocket (to $130 in March 2022).

Yet none of these events and episodes overturns the crucial point: over the past half-century, crude oil’s CPI-adjusted price has fallen an average of 0.2% per year (Figure 5). Since 1960 it’s risen 2.0% per year. This latter result implies that OPEC has exerted a permanent upward impact upon prices.

The Longer Is the Period of Time, the More “Predictable” Is the Price

Thus far, my analysis has produced three key results: 

1. in the short term, nobody can reliably predict commodities’ prices;
2. over shorter intervals, they occasionally boom and bust;
3. over very long terms, CPI-adjusted prices change little.

Table 1, which presents mean CAGRs and their measures of dispersion (standard deviations) over various intervals, summarises my fourth and most important result. It demonstrates why you can’t predict next year’s commodity prices – but can plausibly infer them decades from today.

Reading down the columns, the key point is that as CAGRs lengthen their observations’ standard deviations (SDs) shrink consistently and cumulatively drastically. Comparing oil’s 12-month and 50-year CAGRs, for example, the SD collapses (1.23 – 50.85) ÷ 50.85 = 98%.

Table 1: CPI-Adjusted CAGRs, Crude Oil and Iron Ore Prices, January 1960-June 2025

Over each interval except 65 years (which contains just a handful of observations), both commodities’ observations are randomly distributed (apart from “fat tails”). Take iron ore as an example. Over 12-month intervals the 95% confidence interval is very wide: 2.9% ± (2 × 28.0%) = -53.1% to 58.9%.

Will next year’s price plunge, remain stable or soar? Who knows? Over periods of 50 years, however, the situation is entirely different.

Assuming a normal distribution with a mean of 0.63% and standard deviation of 0.68%, and that iron ore remains necessary for the manufacture of steel, the price remains unpredictable but the 95% confidence interval is much narrower: -0.73% to 2.0%. Over the next 50 years, will the CPI-adjusted CAGR plunge or soar? Neither event is probable. Will it gravitate close to 0%, i.e., will the price remain much the same? That’s far more likely. On this basis, plausible inference – including miners’ plans and investors’ valuations – becomes possible.

It’s vital to be as clear as I can: statistically speaking, very long-term CAGRs remain unpredictable. Their observations not only derive from normal distributions: their “fat tails” greatly abate. However, because their standard deviations – and thus their 95% confidence intervals – shrink drastically, the range of likely results reduces dramatically. In this practical sense and over very long terms, commodities’ prices CAGRs become “predictable” (note the quotation marks).    

Given these assumptions, over the next half-century and beyond the price of iron ore will, on average and adjusted for CPI, continue to do what it’s done over the past 50 years: remain roughly constant. The price of Brent crude will rise slightly. Similar points apply to coal, copper, crude oil, LNG, etc.

Rebutting a Predictable Criticism

Next’s year’s commodity prices are usually unpredictable. Criticisms of hydrocarbons’ long-term future prices, on the other hand, are highly predictable: “haven’t you heard of the energy transition? ‘Net Zero’ by 2050? Everybody knows that during the next quarter-century the demand for fossil fuels – and hence their prices – will collapse. Coal, crude oil, gas and LNG infrastructure are thus “stranded assets,” and their owners will suffer huge losses!”

This criticism is easy to refute. For the sake of brevity and simplicity, let’s consider two contrasting scenarios:

1. OPEC expects that during the 25 years to 2050 the world’s daily consumption of crude oil will increase from the current rate of almost 102 million barrels per day (mbpd) to ca. 120 mbpd. That’s an increase of 18% and a CAGR of 0.7% per year.
2. Over the next quarter-century, the International Energy Agency’s (IEA’s) “net-zero scenario” foresees a collapse of consumption to less than 25 mbpd. That’s a decrease of 76% from the current level and a CAGR of -5.5% per year.

The affordability and security of energy in all nations, and strong growth of demand in developing countries, underpin OPEC’s relatively bullish outlook. The anticipation of a rapid acceleration and cumulatively massive shift towards intermittent (e.g., solar, wind and other “renewable”) sources of energy, which must occur in order to come anywhere near the targets of the UN’s Paris Agreement, underlies IEA’s conjecture.

At the outset, it’s reasonable to doubt IEA’s forecast.

“In recent years,” reported The Wall Street Journal (“Climate Politics Neuters an Energy Watchdog,” 24 February 2024), IEA “has succumbed to politicization.” Most notably, in 2020 it “bowed to enormous pressure from climate activists ...” Specifically, its forecasts anticipated what activists condemned as “unacceptable” long-run demand for oil and gas demand. In WSJ’s words, IEA initially “assumed only the laws currently on the books and (nothing about) future green policies.” Subsequently, however, it succumbed to zealots’ demands; consequently, its “demand forecasts now reflect wishful thinking about the timing and cost of a peak in oil and gas consumption.”

Beyond IEA’s pandering to extremists and resultant questionable credibility, for two additional sets of reasons it seems to me that OPEC’s expectation is reasonable – and that IEA’s is at best far-fetched and at worst absurd.

Firstly, since 1900 the world’s consumption of crude oil hasn’t merely risen almost continuously; presently it shows no sign that it’s decelerating – never mind decreasing. Moreover, the rare occasions when consumption has temporarily fallen have had nothing whatever to do with “climate action.”

Using data from the Statistical Review of World Energy (2025), Figure 7 plots the world’s daily consumption of crude oil. It’s risen from an average rate of 0.450 million barrels per day (mbpd) in 1900 to 101.8 mbpd in 2024. That’s a CAGR of 4.5% per year for almost 125 years. During the Great Depression, consumption fell from 4.6 mbpd in 1929 to 4.0 in 1932; that was a decrease of 13%. During the first (Yom Kippur) oil shock and energy crisis of the early-1970s, consumption decreased from 64.3 mbpd in 1974 to 61.3 in 1975; that was a fall of 5%.

Figure 7: Global Consumption of Crude Oil, Millions of Barrels/Day, 1900-2024

During the second (Iranian Revolution) oil shock and energy crisis of the late-1970s, consumption plunged from 72.5 mbpd in 1979 to 61.9 in 1983.That was almost 15% – more than during the Great Depression. During the GFC, consumption decreased from 88.2 mbpd in 2008 to 86.0 in 2009; that was a decrease of 2.5%. Finally, during the COVID-19 pandemic consumption fell from 100.5 mbpd in 2019 to 93.9 in 2020. That was a total decrease of 6.6% – a bit more than during the first oil shock, but much less than during the second.

Since 1900, economic and geo-political shocks have occasionally but briefly affected the world’s consumption of crude oil; in sharp contrast, “climate action” since the 1990s has exerted no discernible impact.

Figure 8, which plots consumption’s annual percentage changes and ten-year CAGRs, elaborates this key result. Short-term results oscillate around CAGRs – which averaged 6.8% per year until 1979 but just 1.0% per year since 1980. That long-term – indeed, permanent – downwards shift is seemingly a consequence of the second oil crisis.

Figure 8: Global Consumption of Crude Oil, Annual Percentage Changes and Ten-Year CAGRs, 1900-2024

Since 1980, the long-term rate of growth of global crude oil consumption has been remarkably stable; hence trillions of dollars of “climate action” have altered it not one iota. Yet climate zealots are apparently unfazed: even if it hasn’t already, global demand for crude oil will soon begin to plummet.

For a second set of reasons, I strongly doubt it.

That’s because world’s population has grown from ca. 1.6 billion in 1900 to approximately 8.2 billion in 2024 (Figure 9a). That’s a CAGR of 1.3% per year for 124 years. In today’s high-income countries, however, population has grown much more slowly: from 0.5 billion in 1900 to 1.4 billion in 2024. That’s a CAGR of 0.8% per year. The population of low-income countries has grown twice as fast (CAGR of 1.6% per year).

Figure 9a: Global Population, Billions, 1900-2024

Figure 9b, which plots population growth’s ten-year CAGRs globally and in low- and high-income countries (for the sake of legibility I’ve omitted upper-middle and lower-middle income countries), elaborates this result. During the first half of the twentieth century, each series’ rate of long-term growth was ca. 1.0% per year. Over the 15 years to ca. 1960, population growth accelerated in all countries. During these years, the ten-year CAGR increased to ca. 1.25% per year in high-income countries. Overall, it rose more (to 1.75%), and in low-income countries it accelerated most (to almost 2.5%).

Figure 9b: Population Growth, Ten-Year CAGRs, 1910-2024

Moreover, the population boom peaked earliest (ca. 1960) in high-income countries, later (early-1970s) globally and latest (ca. 1990) in low-income countries. Finally, in all countries the CAGRs have decelerated from their peaks. Ten-year growth rates are now lowest (less than 0.5% per year) in high-income countries and highest (almost 1.5% per year) in low-income countries.

What are the implications for the consumption of crude oil in particular and hydrocarbons in general? Figure 10, which plots per-capita consumption of hydrocarbons (coal, crude oil, gas and LNG) in low-income, middle-income and high-income countries from a common base, summarises them:

Figure 10: Per Capita Consumption of Hydrocarbons (1965=100), by Income Level, 1965-2024

1. In high-income countries, whose populations are growing relatively slowly, per capita consumption of hydrocarbons has risen 46% since 1965 (that is, from 100 in 1965 to 146 in 2024); that’s a CAGR of 0.6% per year;
2. In middle-income countries, where population is growing more quickly, per capita consumption has risen 142% since 1965; that’s a CAGR of 1.5% per year;
3. In low-income countries, whose populations are growing most rapidly, per capita consumption has risen 439% since 1965; that’s a CAGR of 2.9% per year.

“What everybody knows” couldn’t be more wrong: worldwide, no “energy transition” is occurring. Thanks in particular to high rates of growth in low-income countries, hydrocarbons’ per capita consumption shows no sign of slowing – never mind decreasing in absolute terms.

Crucially, Figure 10 plots the change of consumption from a common base. In absolute terms, per capita consumption of hydrocarbons in 1965 was ca. 30 times greater in rich than in poor countries; by 2024, the gap had narrowed to 11 times. It’s reasonable to infer that (1) over the next quarter-century and beyond the world’s population will continue to rise but its rate of growth will continue to decelerate, and (2) poor nations’ economies will grow and per capita incomes increase relative to rich nations’ – and therefore that their “hydrocarbons consumption gap” will continue to narrow.

As it always has, so it is now and presumably always will be: the world’s climate evolves. It’s a constant – and hardly the biggest challenge facing the world. Far more important and urgent is the reduction of global poverty – including energy poverty (see also Why fossil fuels are ethical and their opponents aren’t, 19 December 2022).

On these bases, it’s reasonable to expect that per capita consumption of hydrocarbons in poor countries will continue to rise rapidly, that in middle-income countries it will continue to lift modestly, and that in rich countries it likely won’t fall appreciably – if at all. Meanwhile, the population of low-income countries will increase relatively rapidly and of high-income countries will rise comparatively slowly.

Hence climate crazies’ criticism of my results – like their policies – fails. As Chris Uhlmann (“Progress and prosperity fired by fossil fuels,” The Weekend Australian, 30-31 August) notes, “there is no energy transition. Every new fuel has been an addition to a pile that grows ever higher. Wind and solar add only a sliver to that pile and will never be the dominant source of primary energy.”

As the world’s population and per capita consumption of hydrocarbons rises, the total consumption of hydrocarbons won’t fall. Quite the contrary: it’ll continue to increase. My analysis and assumptions thus corroborate OPEC’s expectation and contradict IEA’s.

Conclusions

“There is no way to predict the price of stocks and bonds over the next few days or weeks,” The Royal Swedish Academy of Sciences stated on 14 October 2013 in the press release that announced that year’s Sveriges Riksbank (Bank of Sweden) Prize in Economic Sciences in Memory of Alfred Nobel (which is universally but erroneously known as “The Nobel Prize in Economics”). 

This crucial point also holds over periods of at least 12 months and occasionally as long as two years: during these intervals, equities’ prices – and thus their returns – fluctuate mostly randomly and thus unpredictably (for details, see Stop kidding yourself: Nobody can “time the market” 30 June).

“But,” the Academy’s announcement continued, “it is quite possible to foresee the broad course of these prices over longer periods, such as the next ... five years” and beyond. “These findings,” it elaborated, “which might seem both surprising and contradictory, were made and analyzed by this year’s Laureates, Eugene Fama, Lars Peter Hansen and Robert Shiller.”

In this article, I’ve demonstrated that a similar point applies to the prices of globally-traded commodities – including the biggest, crude oil, and Australia’s most significant mineral and energy exports (coal, copper, iron ore and LNG).

Given their CAGRs over the decades, and bearing in mind the near-certainty that geo-political, macro-economic and other factors will at various junctures and for various durations cause particular commodities’ prices to skyrocket and plunge, it’s reasonable to assume that their 50-year CAGRs to 2075 will, by and large and depending upon the commodity in question, vary within the range -2.0% to + 2.0% per year.

In the short term, commodities fluctuate greatly. They’re also normally distributed – and thus mostly (that is, except at extremes) unpredictable. Over long terms and adjusted for CPI, however, despite sharp upsurges and severe downdraughts, (1) CAGRs’ average fluctuations tamp considerably and (2) their dispersion decreases dramatically: on average, CPI-adjusted prices either rise little, remain stable or fall slightly. 

In these crucial senses, they’re roughly foreseeable. Although short-term disruptions to supply can and do cause price spikes, over long periods of time producers tend to adjust their output in response to these and other signals. This constant process of adjustment returns supply and demand to balance.

Hence it’s long been clear that – and why – intermittent (i.e., solar and wind) energy doesn’t displace hydrocarbons (see, for example, Richard York, “Energy transitions or additions? Why a transition from fossil fuels requires more than the growth of renewable energy,” Energy Research & Social Science, vol. 51, May 2019).

On 25 February, in Foreign Affairs – an icon of the establishment – Lazard Chairman and Obama administration brains-truster, Peter Orszag, together with Daniel Yergin and Atul Arya of S&P Global, acknowledged what should be obvious to everybody – but zealots of intermittent energy nonetheless continue resolutely to ignore or vehemently to deny. In an article entitled “The Troubled Energy Transition,” they showed that, “despite record growth in wind and solar energy, hydrocarbons like oil and coal also hit all-time highs, indicating an ‘energy addition’ rather than a true transition.”

“Rather than replacing conventional energy sources,” they concluded, “the growth of renewables is coming on top of that of conventional sources.” In other words, there simply is no “energy transition.”

“No kidding,” added Holman Jenkins (“End of a Green Delusion,” The Wall Street Journal, 18 August). If this crucial fact had been recognised, “a world-historical boondoggle might have been avoided long ago. It needed only a media that did its job of holding a mirror up to reality rather than pathetically (flattering their inflated) self-images. For some greens the wake-up call will never come. They’re little more than shills for (privileged) industries ... Their survival depends on direct government support for fake solutions ... The damage to other things Americans care about, such as jobs and economic dynamism, has been incalculable.”

In his recent article, Jenkins recalled that in 2016 he declared that “the only live question was how much the U.S. would spend on climate change to have no effect on climate change. The answer will be in the trillions.” With almost a decade of hindsight, the global wastage has likely been tens of trillions of $US. In Australia it’s been scores of billions – and rising rapidly.

Implications

“In analysing an individual company,” wrote Benjamin Graham in Chap. 38 of Security Analysis, “operating results must be scrutinized for signs of possible unfavourable changes in the future. This procedure may be illustrated by various examples drawn from the mining field.”

In such an analysis, he added: “(we) must also consider ... the future selling price of the (commodities which the company under examination produces). Here we must ordinarily enter into the field of surmise or of prophecy. The analyst can truthfully say very little about future prices, except that they fall outside the realm of sound prediction.”

Graham lacked what we now possess: decades of commodity price and CPI data. Given these advantages and the results of my analysis, we can see what he couldn’t: commodity prices’ CPI-adjusted CAGRs – especially those decades into the future – are well within the realm of plausible inference.

Yet it’s unarguably true: Warren Buffett is the most successful investor since the 1950s – and his vehicle since the 1960s, Berkshire Hathaway, has with a few exceptions (today is one) has seldom owned shares of mining and energy companies. Generally speaking, value investors typically avoid such companies; in particular, they eschew junior miners. That’s because they’re typically explorers, and their exploration risk is always very high. Most exploration projects fail to find economically viable deposits; as a result, they’re highly speculative and usually loss-making.

Mining companies – including the world’s biggest, such as BHP – typically have little control over the prices they receive for the commodities they produce. Additionally, mining is complex and capital-intensive, and these attributes entail various operational risks which can and do unexpectedly and severely impact mining and energy companies’ profitability and returns.

Most importantly, in these sectors “moats” are rare.

This word, whose application to valuation Buffett has popularised, refers to a business’s ability to protect its profitability. Like a castle (company), its moat (depth and breadth of its competitive advantage) defends those inside the fortress (profits) from outsiders (competitors).

Moats in mining and energy are rare because commodities are basic products which are interchangeable with other products of the same type. That’s the definition of a commodity: one producer’s output is essentially indistinguishable from another’s. BHP’s iron ore, for example, is effectively the same as Rio Tinto’s, which is effectively identical to Fortescue’s. Similarly, Santos’ LNG is for all practical purposes identical to Woodside’s. As Warren Buffett cautioned his son (who as a young man wished to become a farmer): “no one goes to the supermarket to buy Howie Buffett’s corn.”

As producers of commodities, mining and energy companies create sustainable competitive advantages only by becoming lowest-cost producers; and they become low-cost producers by acquiring or developing “Tier-1” assets.

These are mineral deposits which are exceptionally large (and can thereby supply a significant percentage of global demand), have exceptionally long life spans (typically 50 years or more) and rank among the world’s lowest-cost to operate. Under these circumstances, mining operations resemble manufacturing operations. Tier-1 deposits typically offer not merely longevity, but also durable and significant economic returns. This point deserves emphasis:

• In 2019, Canberra and WA’s state government approved BHP’s “strategic proposal covering its Pilbara (iron ore) footprint over the next 50-100 years.” These approvals have “streamline(d) project approvals ...”
• Earlier this year, following the WA government last year, Canberra extended to 2070 the lifespan of Woodside’s North West Shelf project.
• According to press reports (“Rio Tinto reveals 80-year plan for Pilbara iron ore mines,” The Australian, 29 July), “Rio Tinto plans to be mining iron ore ... well into the 22^nd century, in another rebuff to Andrew Forrest’s claims the region is in imminent danger of becoming a wasteland.”

For these and other reasons, at multiple junctures since 1999 and also recently, Leithner & Company has purchased the shares of Australian-based and globally-significant owners of Tier-1 mining and energy assets.

Despite some disadvantages – let’s be honest, ALL companies and sectors have SOME drawback(s) – low-cost and globally-significant producers of minerals and energy possess a key advantage which very few other companies do and no other sector does: products whose CPI-adjusted prices can plausibly be inferred decades into the future.

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