The Eight Deadly Sins of Analyzing the Energy Transition
The renewable revolution is plainly gaining speed and impact. So why are so many analysts so wrong about the pace and scale of innovation?
The renewable revolution is advancing at remarkable speed. In fact, the speed of the renewable revolution has defied many leading energy commentators who have continuously underestimated its true trajectory. They have suffered from what statisticians call a systematic bias, that is, an error that consistently skews in one direction. Noise, or a random error, is inherent to forecasting; bias, however, requires a deeper explanation.
So why do so many intelligent people undersell the pace and dynamism of the renewable revolution? Leaving aside the inherent bias of those seeking to prop up the fossil fuel system in order to enjoy the largesse of its annual $2 trillion in rents, we identify eight deadly sins of the energy transition.
Whether intentional or unwitting, these eight general errors of perspective are holding back understanding, wasting time and capital, and fueling unproductive climate pessimism.
1. Linear thinking
The first common error is to think that technology change is linear.
However, technology history is replete with S-shaped curves, not straight lines, from the growth of canals and railways to the internet and mobile phones. These S-curves are driven by the self-reinforcing dynamic of “feedback loops” — change begets change. As technologies grow, they enjoy increasing returns to scale. These include falling costs, rising capabilities, rising consumer awareness, rising lobbying power, and more complementary technologies which all in turn spur greater scale.
This is the source of the exponentiality, and it is often missed. The core new energy technologies of solar, wind, batteries, heat pumps and green hydrogen fit the S-shaped growth patterns of the past. Too often the default position is linear change when this is in fact a contrarian view that thinks this time is different.
2. Lagging indicators
The error is to focus on stocks, which are the installed base that has accumulated over time.
However, stocks are a lagging indicator. Stocks follow flows; the car fleet follows car sales. Flows are the leading indicator. In the early stages of a transition, flows present a very different story to stocks. For instance, it is frequently noted that fossil fuels account for over 80 percent of global primary energy and this number hasn’t budged meaningfully for decades.
Rarely mentioned is the fact that renewables have been taking an increasing share of the growth in energy supply, and all of the growth in 2019-21. Moving the focus from stocks to flows moves the conclusion from no change to radical change. Concentrating on the size of the fossil fuel system today is like focusing on the large number of horses in 1900 — it was as good a guide then as it is now.
By the end of this decade, the core renewable technologies will all dominate sales in their respective areas; solar and wind already make up over 80 percent of the capacity additions in electricity, and by 2030 EVs will be over two-thirds of car sales. Once renewable technologies dominate sales, it is simply a matter of time and depreciation of the old system before they dominate stocks.
3. Turning points
The error is to underestimate the timing and impact of peak demand in the old system. Many argue for example that the impact of the energy transition on the oil sector will be small because oil demand will still be high in 2030.
In 2030, demand for oil will indeed still be high, but peak oil will clearly be behind us, and with peaks comes disruption. In capital-intensive industries with high operational leverage, such as fossil fuels, marginal reductions in demand can lead to large changes in prices and profits. Incumbents build for growth, and when there is no growth, they face overcapacity, lower prices and stranded assets. Stranded assets are inevitable when linear fossil fuel retirement rates meet exponential demand destruction.
Once capital markets think an industry faces terminal decline, they do not wait for the last barrel to be sold before they disinvest in the old industry and reinvest in its successor. And this capital reallocation further speeds up change, a process George Soros called ‘reflexivity’. This is one of several vicious spirals of descent that kick in following peak demand. Others include rising unit costs, declining political capital, dwindling public support, and a drying up talent pool. As Seneca remarked 2,000 years ago: Fortune is of sluggish growth, but the way to ruin is rapid. Or, in more prosaic terms: Equities take the stairs up but the elevator down.
4. Hardest to solve
The error is to think that the hardest-to-solve sectors and countries hold back the rising tide of change.
The deployment ceiling of the easier-to-solve sectors is well above our heads. The largest areas of fossil fuel demand are the most vulnerable. Power, road transport, and low-temperature heat make up nearly 70 percent of fossil fuel demand, and they already face the threat of successful and rapidly growing renewable technologies.
Moreover, this ceiling of the possible is rising as scientists and entrepreneurs work to develop new solutions. As the easy areas are solved, the hard areas get easier. As the cost of new technology falls and the performance rises, it expands into one market after another. Technology change is sequential: it moves from easy to hard areas; from simple to complex; from light to heavy; and from early to late adopters. Batteries did not jump from hand-held calculators to trucks; they moved from large electronics to cars to SUVs and then onto trucks.
The default assumption should therefore be a rising ceiling of the possible, even where it is not clearly visible which of the many new technology options will be the most significant. Specifically, three ceilings will continue to rise: the ceiling of variable renewables, electrification, and profitable-to-decarbonize. As the energy sector is liberated from carbon, it is only just beginning to discover the periodic table, as Harry Benham notes. With new-found options to shift from lithium to sodium to iron, the possibility space is expanding faster than we can map it. We are familiarizing ourselves with Orgel’s Second Rule that evolution is cleverer than we are. That is to say: The bottom-up creativity of thousands of innovators continuously probing the possibility space beats the imagination of those sitting in Parisian conference rooms trying to fill out spreadsheets.
5. Static world
The error is to assume static technologies, policies, business models and societal perceptions.
During a technology revolution, it is important to assume most relevant factors are dynamic not static, or are variables not constants as Jamie Arbib notes. Common variables that are dynamic include: renewable costs which fall as deployment rises; renewable deployment which rises as costs fall; policy action which advances as the climate emergency intensifies. Furthermore, many of these variables feed off each other.
By 2030, solar — the cheapest energy source in history — is likely to halve in cost, further moving the Overton Window of what is possible, further incentivizing energy-intensive industries to follow the sun, and further increasing the returns to electrification. In 2023, it is hard to think this through, let alone to model it in detail.
Many models freeze today’s solutions and apply them to tomorrow’s challenges. For example, tomorrow’s high levels of ‘variable renewables’ are mapped on today’s technologies, business models and consumption patterns, leading to the incorrect idea that every solar panel needs to be backed up all the time. In reality, tomorrow’s highly digitalized grid and tomorrow’s responsive households will favor consuming electricity when it is cheapest. Carbon capture and storage is advanced as a solution for the sectors that we today find hard to solve, ignoring the fact that it is a high-cost solution which will have to compete against superior technologies on learning curves.
Mapping tomorrow’s energy system onto today’s world worked fine during a long period of relative stasis in the fossil fuel energy system. But it fares poorly at a time of transformational change. Buckminster Fuller’s analogy comes to mind: There is nothing in a caterpillar that tells you it’s going to be a butterfly. As RethinkX articulates, the energy future is not a modified version of the recent past, but a transformation to a new system. One consequence of transformations is they can render experts in caterpillars poor guides to a world of butterflies.
6. Climate streetlight
The error is to look only under the climate streetlight, thinking the sole driver of the energy transition is halting climate change.
The climate was the spark and remains a constant and powerful catalyst, but the forces of change are deeper and the motivations more diverse. What lies at the heart of the energy transition is a shift from an expensive, inefficient, volatile, scarce, commodity-based fossil system to cheaper, cleaner, leaner technologies that offer continuously falling costs and are available everywhere. We are moving from heavy, fiery molecules to light, obedient electrons; from hunting fossil fuels to farming the sun. The renewable revolution continues the long arc of energy history: efficiency beats waste, technologies beat commodities, and economics beats ideology.
Looking only under the climate streetlight contributed to the misconception that Putin’s War would slow the energy transition, as no one would be able to ‘afford to be green’. It means many are surprised that China is the leader of the energy transition: Why would a country that prioritizes development prefer to invest so much into climate mitigation? It leads to a focus on band-aid solutions like carbon capture and storage. It leads to thinking in terms of carbon footprinting and ESG, not risk and opportunity.
7. Understating energy efficiency
The error is to pay little attention to energy efficiency as a driver of the energy transition.
However, energy efficiency has thus far driven more emission reductions than renewables. For example, since 2010 gains in energy intensity have averaged 1.7% a year, saving about ten times as much primary energy as solar and wind added, according to IEA’s data. In 2022, energy efficiency saved over 3 times as much primary energy as the growth of solar and wind. Despite the magnitude of efficiency gains, renewables get nearly all the headlines. As Amory Lovins remarks, solar panels are highly visible whereas unused energy is invisible, almost unimaginable and as a result gets little attention. Lovins first highlighted the power of energy efficiency to drive change 40 years ago and it has been a constant and underestimated feature of the energy transition since.
Furthermore, if the efficiency gains of the past were large, they are about to get larger. There are still huge untapped opportunities for energy efficiency to wring more work from fewer inputs. Material innovation, integrative design and more digitization will continue to create smarter, leaner and lighter energy systems. In addition to this, the shift from inefficient fossil-fueled electricity generation to solar and wind uses around 60% less primary energy, the shift from oil to electricity in transport uses around 75% less primary energy, and the shift from thermal boilers to heat pumps uses around 75% less primary energy. As these technologies continue to grow on their S-curves, so they will increase the annual rate of efficiency gains. As a result, the aspiration to double efficiency gains by 2030 is much more achievable than commonly perceived.
8. Lost in complexity
The error is to model excessive complexity.
In systemic shifts, modeling huge complexity and detail is a curse, not a blessing. There is no point in taking pride in your model with 1.6 million variables if you can’t even forecast solar costs right.
Models are a bit like greengrocers: From afar, a very wide and exotic range of offerings appears tempting. But as you look closer, the wider the range, the harder it is to ensure any of it is kept fresh; the harder it is to ensure quality. Rotting out-of-date vegetables, much like out-of-date model inputs, are of little value. At times of rapid change, models that use data that is two or three years old and fail to incorporate learning curves will end up with profoundly incorrect results. If you model everything, you model nothing well. To paraphrase Doyne Farmer reflecting on the Oxford INET model: It is about empirically representing technology dynamics using as few variables as possible.
Equally important is to strike a better balance between thinking and modeling. Technology revolutions are intrinsically imprecise, involving important realities that are hard or impossible to credibly quantify. The same is true for the risks of climate change. Thinking is better at dealing with the unquantifiable than mathematical models. In these contexts, judgment about risks and opportunities will often be superior to conventional cost and benefit analysis.
New thinking for faster change
Misrepresenting the speed and dynamism of the renewable revolution is not a recipe for success. The issue is not just that it is wrong — it is also self-fulfilling. It leads to wasted time and capital, lost competitiveness and unproductive climate pessimism.
Technology revolutions are never linear. The timing of change matters; flows come before stocks; easy areas come before hard; market disruptions come early not late. They are periods of flux, not equilibrium, and periods of flux are inherently imprecise. Efficiency is an invisible superpower. And the drivers of technology revolutions are deep and diverse.
This matters because expectations matter: we build the future that we expect. The renewable revolution will come faster and take fewer by surprise if we avoid these deadly sins.
Thanks to Jamie Arbib, Harry Benham, Amory Lovins, Chris Nelder, Laurens Speelman, Simon Sharpe, and Daan Walter for their ideas and suggestions.